Charging method, system and device of electric scooter and storage medium

Abstract

The invention relates to a charging method, system and device for an electric scooter and a storage medium, and the method comprises the steps: obtaining the parameters of a battery pack of the electric scooter, and carrying out the health state analysis of a battery, and obtaining the initial state data of the battery; acquiring charger type information of the electric scooter, and performing matching analysis on the charger type information and the battery initial state data to obtain target charging information; performing charging simulation based on the target charging information, and starting equalizing charging control according to the voltage difference of the single batteries to obtain charging control parameters; setting a charging protection threshold value according to the charging control parameter, and carrying out strategy construction on the charging protection threshold value and the target charging information to obtain a charging protection strategy; and performing charging control on the electric scooter based on the charging protection strategy, and performing charging identification to obtain a charging record. According to the method, the charging parameters can be optimized by combining the charger type information and the battery initial state data.

Description

Technical Field

[0001] The present invention relates to the technical field of electric scooters, and in particular to a charging method, system, device and storage medium for an electric scooter. Background Art

[0002] As an environmentally friendly and convenient short-distance travel tool, electric scooters have played an increasingly important role in urban transportation in recent years. With the rapid expansion of the electric scooter market, users' requirements for charging safety, battery life and charging efficiency are also increasing. How to optimize the charging process of electric scooters so that it can not only meet users' fast charging needs but also ensure the long-term service life of the battery has become one of the research focuses. Existing electric scooter charging methods often adopt a simple constant current and constant voltage charging strategy, which only considers basic charging parameters such as charging voltage and current, while ignoring the health status of the battery pack, the differences between single cells, and the impact of the charger type on the charging process. This simplified charging method may lead to problems such as unbalanced battery charging, overcharging or over-discharging, thereby shortening the battery life and even causing safety hazards. At the same time, due to the lack of an effective charging record and analysis mechanism, it is difficult for users to understand the health status and charging efficiency of the battery in a timely manner, which is not conducive to the long-term maintenance and management of electric scooters. With the continuous development of battery technology and the improvement of users' performance requirements for electric scooters, it is particularly important to develop an intelligent charging method that can comprehensively consider factors such as battery health status, charger type, and single cell balancing. Summary of the invention

[0003] The main purpose of the present invention is to provide a charging method, system, device and storage medium for an electric scooter, which can optimize charging parameters by combining charger type information and battery initial state data.

[0004] To achieve the above object, the present invention provides a charging method for an electric scooter, comprising: Obtain the battery pack parameters of the electric scooter, perform battery health status analysis, and obtain the battery initial status data; Obtaining charger type information of the electric scooter, and performing matching analysis with the battery initial state data to obtain target charging information; Performing charging simulation based on the target charging information, and starting balanced charging control according to the voltage difference of the single cells to obtain charging control parameters; Setting a charging protection threshold according to the charging control parameter, and constructing a strategy with the target charging information to obtain a charging protection strategy; Based on the charging protection strategy, the electric scooter is charged and identified to obtain a charging record.

[0005] Furthermore, the battery pack parameters of the electric scooter are obtained, and the battery health status analysis is performed to obtain the battery initial status data, including: Identify the battery pack of the electric scooter to obtain the battery pack parameters including the number of charge and discharge times and the usage time; Measuring the battery internal resistance of the electric scooter according to the battery pack parameters to obtain a battery internal resistance characteristic value; Real-time sampling of the battery cell voltage, total voltage and charging current of the electric scooter to obtain battery operating status data; Calculate the coulomb efficiency according to the battery internal resistance characteristic value and the battery operating status data to obtain the battery capacity attenuation coefficient; Performing piecewise function mapping on the battery capacity attenuation coefficient to obtain a battery health level determination value; The state is constructed according to the battery health level determination value and the battery operation state data to obtain the battery initial state data.

[0006] Furthermore, the acquiring of the charger type information of the electric scooter and matching analysis with the battery initial state data to obtain target charging information includes: Sampling and detecting the voltage and current signals of the charger of the electric scooter to obtain characteristic parameters of the charger; Performing type matching on the characteristic parameters of the charger according to preset type data to obtain the charger type information; Performing a charging safety assessment on the charger type information and the battery initial state data to obtain a charging safety level; Performing a hierarchical analysis on the charging safety level to obtain a hierarchical charging parameter; Target identification is performed according to the graded charging parameters and the charger type information to obtain the target charging information.

[0007] Furthermore, the charging simulation is performed based on the target charging information, and balanced charging control is started according to the voltage difference of the single cells to obtain charging control parameters, including: Extracting parameters of the target charging information to obtain charging current time series data; Performing numerical integration calculation according to the charging current time series data to obtain a charging capacity curve of the battery pack; Performing statistical analysis on the battery initial state data to obtain single cell battery voltage distribution characteristics; Performing index variance calculation on the voltage distribution characteristics of the single cell to obtain a voltage deviation index; A compensation coefficient is set according to the voltage deviation index to obtain a balance adjustment parameter; Verifying the validity of the balance adjustment parameter to obtain a balance trigger threshold; Prioritizing the equalization triggering thresholds to obtain an equalization adjustment sequence; Calculate the compensation amount according to the balance adjustment sequence to obtain a balance correction value; The charging capacity curve is corrected according to the equalization correction value to obtain a charging control parameter.

[0008] Furthermore, the battery pack charging capacity curve is obtained by performing numerical integration calculation according to the charging current time series data, including: Dividing the charging current time series data into time windows to obtain sampling sub-intervals; Calculating the endpoint function value of the sampling sub-interval to obtain the current value of the interval boundary point; Performing current integration on the current value at the boundary point of the interval according to a preset Simpson coefficient to obtain an initial value of current integration; Performing interval step correction on the initial value of the current integration to obtain a corrected capacity increment; Performing accumulation operation according to the capacity increment to obtain a cumulative capacity sequence; Performing Kalman filtering on the accumulated capacity sequence to obtain a state estimation result; An adaptive control model is constructed according to the state estimation result, and dynamic capacity planning optimization is performed to obtain target capacity state information; Curve mapping is performed according to the target capacity state information to obtain the battery pack charging capacity curve.

[0009] Furthermore, the charging protection threshold is set according to the charging control parameter, and a strategy is constructed with the target charging information to obtain a charging protection strategy, including: Performing threshold analysis on the charging control parameter to obtain a charging voltage threshold and a charging current threshold; Perform charging state analysis according to the charging voltage threshold and the charging current threshold to obtain battery pack charging state data; Performing safety parameter analysis on the battery pack charging state data and the target charging information to obtain charging safety parameters; Dynamically adjust the charging process according to the charging safety parameters to obtain a charging adjustment strategy; Performing threshold extraction on the charging regulation strategy to obtain a charging protection threshold interval; hierarchically processing the target charging information according to the charging protection threshold interval to obtain hierarchical charging protection data; Performing charging strategy matching on the hierarchical charging protection data according to a preset strategy database to obtain an initial protection strategy; The charging control parameters of the initial protection strategy are configured to obtain a charging protection strategy.

[0010] Furthermore, the charging control of the electric scooter based on the charging protection strategy and the charging identification to obtain the charging record include: Performing instruction parsing on the charging protection strategy to obtain a charging control instruction set; Performing segmented charging control on the electric scooter according to the charging control instruction set to obtain segmented charging data; Performing real-time monitoring on the segmented charging data to obtain charging status information; Performing voltage sampling on the electric scooter according to the charging status information to obtain voltage sampling data; Performing difference calculation on the voltage sampling data to obtain voltage balancing parameters; Dynamically adjusting the charging current of the electric scooter according to the voltage equalization parameter to obtain an adjusted charging current value; Performing charging control on the adjusted charging current value to obtain real-time charging data; Performing feature extraction processing according to the real-time charging data to obtain a charging feature value; Identify and classify the charging characteristic values ​​to obtain a charging type identifier; The charging of the electric scooter is monitored according to the charging type identifier to obtain the charging record.

[0011] The present invention further provides a charging system for an electric scooter, which is applied to any of the above-mentioned charging methods for an electric scooter, comprising: A collection module, which is used to obtain battery pack parameters of the electric scooter and perform battery health status analysis to obtain battery initial status data; An analysis module, the analysis module is used to obtain the charger type information of the electric scooter, and perform matching analysis with the battery initial state data to obtain target charging information; An association module, the association module is used to perform charging simulation based on the target charging information, and start balanced charging control according to the voltage difference of the single cells to obtain charging control parameters; A processing module, the processing module is used to set a charging protection threshold according to the charging control parameter, and construct a strategy with the target charging information to obtain a charging protection strategy; A control module is used to control the charging of the electric scooter based on the charging protection strategy, and perform charging identification to obtain a charging record.

[0012] The present invention also provides a charging device for an electric scooter, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of the charging method for an electric scooter described in any one of the above.

[0013] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0014] The present invention provides a charging method, system, device and storage medium for an electric scooter, which have the following beneficial effects: By obtaining the battery pack parameters and analyzing the battery health status, the working status and performance indicators of the battery can be mastered in real time, providing a reliable basis for formulating a reasonable charging strategy and effectively avoiding the battery loss problem caused by simple charging methods. By matching and analyzing the charger type information with the battery initial status data, the charging parameters can be optimized according to the characteristics of different chargers to ensure the safety and efficiency of the charging process, overcoming the defect of insufficient consideration of the charger characteristics in traditional charging methods. Through charging simulation and balanced charging control, the voltage difference problem between single cells can be effectively solved, local overcharging or over-discharging can be avoided, and the service life of the battery pack can be extended. According to the charging control parameters, the charging protection threshold is set and the charging protection strategy is constructed to achieve all-round protection of the charging process, effectively prevent the battery from being damaged by improper charging, and improve the safety of the electric scooter. Charging control and identification based on the charging protection strategy can not only ensure the safety and reliability of the charging process, but also generate detailed charging records, which is convenient for users to understand the battery usage status and charging efficiency, and provide data support for the daily maintenance and management of electric scooters. Through the intelligent charging control system, the refined management of the battery charging process is realized, the charging efficiency is improved, and the battery life is extended, meeting the user's demand for long-term and stable use of electric scooters. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a charging method for an electric scooter provided by the present invention; Figure 2 It is a structural diagram of a charging system of an electric scooter provided by the present invention; Figure 3 It is a structural diagram of a charging device for an electric scooter provided by the present invention.

[0016] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Reference Figure 1 As shown, the present invention also provides a charging method for an electric scooter, comprising: Step S1: obtaining battery pack parameters of the electric scooter, and performing battery health status analysis to obtain battery initial status data; Step S2: Obtain the charger type information of the electric scooter, and perform matching analysis with the battery initial state data to obtain target charging information; Step S3: performing charging simulation based on the target charging information, and starting balanced charging control according to the voltage difference of the single cells to obtain charging control parameters; Step S4: setting a charging protection threshold according to the charging control parameter, and constructing a strategy with the target charging information to obtain a charging protection strategy; Step S5: Based on the charging protection strategy, the electric scooter is charged and identified to obtain a charging record.

[0020] Based on the above steps, the detailed process is as follows: Step S1: The battery pack parameter collection includes key data such as the total voltage of the battery pack, the voltage of the single cell, the temperature of the battery pack, and the internal resistance of the battery. By establishing a battery equivalent circuit model, the collected voltage and temperature data are used to calculate the state of charge (SOC) and state of health (SOH) of the battery. The battery health status analysis adopts a dual evaluation method based on internal resistance and capacity decay, comparing the measured internal resistance value with the nominal internal resistance, and estimating the ratio of the actual capacity of the battery to the rated capacity. The battery initial status data contains the following information: battery remaining capacity percentage, single cell voltage distribution, battery pack temperature distribution, internal resistance aging degree, etc. These data provide a basic basis for the subsequent charging process to ensure that the charging strategy can adapt to the actual state of the battery. The battery status data also includes historical charging and discharging times, deep cycle times and other usage records, which are used to evaluate the battery life and performance decay trend. By analyzing the historical data and current state of the battery, the performance characteristics of the battery during the charging process can be predicted, providing an important reference for formulating a reasonable charging strategy.

[0021] Step S2: The charger type information of the electric scooter covers the rated output voltage, maximum output current, charging curve characteristics and other technical parameters of the charger. The matching analysis of the charger and the battery is based on the data processing capability of the battery management system (BMS), and the charger output characteristics and the battery initial state data are comprehensively evaluated. The generation process of the target charging information takes into account the power limitation of the charger and the charging acceptance capacity of the battery, including specific indicators such as the maximum charging current, charging cut-off voltage, and charging time estimation. During the matching analysis process, the optimal charging current curve is calculated in combination with the temperature characteristics and internal resistance changes of the battery to ensure a balance between charging efficiency and safety. The target charging information also includes the division of charging stages, such as the specific parameter settings of the pre-charging stage, the constant current charging stage, and the constant voltage charging stage, to provide an accurate execution basis for subsequent charging control.

[0022] Step S3: Charging simulation is based on the battery equivalent circuit model, and the voltage, current and temperature changes during the charging process are predicted by numerical calculation methods. Balanced charging control uses active balancing technology to transfer the energy of high-voltage batteries to low-voltage batteries based on the voltage difference of single cells in the battery pack. The charging control parameters are optimized through simulation results, including key parameters such as the balanced starting voltage difference threshold, balanced current size, and balanced time window. The polarization effect and temperature influence of the battery are considered during the simulation process, and the optimal charging current waveform is determined through iterative calculation. The generation process of charging control parameters also includes the prediction of battery temperature changes to ensure that the battery temperature always remains within a safe range during charging. The formulation of the balanced charging control strategy aims to improve the consistency of the battery pack. Through the precise setting of the balanced control parameters, the synchronous charging and performance optimization of each single cell of the battery pack can be achieved.

[0023] Step S4: The charging protection threshold is set based on the safe operating range and charging control parameters of the battery pack to achieve effective protection against abnormal conditions such as overcharging, overvoltage, overcurrent and overtemperature. The protection threshold calculation comprehensively considers the physical characteristics and safety margin of the battery pack, including the maximum charging voltage limit, the maximum charging current limit, the upper temperature threshold and the temperature gradient limit. The charging protection strategy construction process integrates the target charging information with the protection threshold to form a multi-level protection mechanism. The strategy construction adopts a state machine model to define the conversion conditions and protection response measures for different charging stages. The charging protection strategy also includes a gradient protection mechanism, which achieves a smooth transition of protection measures by setting early warning thresholds and trigger thresholds. The core of the protection strategy is to establish a dynamic mapping relationship between charging parameters and protection thresholds to ensure that the protection mechanism can respond in time at different charging stages and battery states to ensure the safety and reliability of the charging process. The charging protection strategy takes into account both charging efficiency and battery life, and maximizes the charging speed and battery life cycle while meeting safety requirements.

[0024] Step S5: The charging control process based on the charging protection strategy is implemented through the battery management system to dynamically adjust the charging current and voltage parameters. The charging control follows the multi-stage charging curve and realizes smooth switching between pre-charging, constant current charging and constant voltage charging according to the battery charge state. The charging identification technology identifies the charging state and abnormal conditions by analyzing the characteristics of the voltage and current curves during the charging process. The charging record contains complete data of the charging process, such as key indicators such as charging time, charging amount, charging efficiency, and temperature change. During the charging control process, the system continuously monitors the single cell voltage and component temperature, and compares them with the protection threshold. Once an abnormality is detected, the corresponding protection measures are immediately triggered. The charging identification algorithm adopts a pattern recognition method to judge the charging quality and potential problems by analyzing the characteristic parameters of the battery voltage response curve. The charging record not only serves as a usage record, but also provides important data support for battery health status assessment and charging strategy optimization, forming a closed-loop charging management system.

[0025] Step S6: After charging is completed, the charging records are comprehensively evaluated to analyze the charging efficiency, charging balance and battery response characteristics. The evaluation indicators include the ratio of charging time to theoretical time, the consistency of single cell voltage, the temperature uniformity of the charging process, etc. Based on the evaluation results, the charging strategy is optimized in a targeted manner, the charging parameters and protection thresholds are adjusted, and an adaptive charging mechanism is formed. The charging strategy optimization adopts a machine learning method to predict the optimal charging strategy by analyzing the correlation between historical charging data and battery performance changes. The optimization process takes into account the changes in battery characteristics under different ambient temperatures and usage scenarios to achieve the environmental adaptability of the charging strategy. The post-charging evaluation also includes a re-evaluation of the battery health status, updating the battery health parameters, and preparing for the next charging. The strategy optimization results are stored in the battery management system to achieve continuous evolution of the charging strategy, extend battery life, and improve the overall performance and user experience of the electric scooter.

[0026] The present invention provides a charging method for an electric scooter. By acquiring battery pack parameters and performing battery health status analysis, the working status and performance indicators of the battery can be grasped in real time, providing a reliable basis for formulating a reasonable charging strategy, and effectively avoiding the battery loss problem caused by a simple charging method. By combining the charger type information with the battery initial state data for matching analysis, the charging parameters can be optimized according to the characteristics of different chargers to ensure the safety and efficiency of the charging process, and overcome the defect that the traditional charging method does not consider the characteristics of the charger enough. Through charging simulation and balanced charging control, the voltage difference problem between the single cells can be effectively solved, the occurrence of local overcharge or over-discharge can be avoided, and the service life of the battery pack can be extended. The charging protection threshold is set according to the charging control parameters and the charging protection strategy is constructed to achieve all-round protection of the charging process, effectively prevent the battery from being damaged by improper charging, and improve the safety of the electric scooter. Charging control and identification based on the charging protection strategy can not only ensure the safety and reliability of the charging process, but also generate detailed charging records, which is convenient for users to understand the battery usage status and charging efficiency, and provide data support for the daily maintenance and management of the electric scooter. Through the intelligent charging control system, refined management of the battery charging process is achieved, which improves the charging efficiency and extends the battery life, meeting the user's demand for long-term and stable use of electric scooters.

[0027] In one embodiment, the battery pack parameters of the electric scooter are obtained, and the battery health status analysis is performed to obtain the battery initial status data, including: The battery health status is analyzed during the charging process of the electric scooter. The battery health status is comprehensively evaluated from multiple dimensions such as the number of charge and discharge times, usage time, internal resistance characteristic value, and operating status data, providing basic data for battery charging management.

[0028] The battery pack identification process collects the basic parameters of the battery pack, including obtaining the charge and discharge cycle data from the battery management system, which directly reflects the frequency of battery use; reading the cumulative usage time data of the battery pack from the system, which includes the charging time, discharging time and static time. These parameters are recorded and stored as basic data for evaluating the intensity of battery use. The results of battery pack identification include two specific values: the charge and discharge cycle value and the usage time value.

[0029] The battery internal resistance measurement link adopts the AC impedance method. When the battery is in a static state, a small AC excitation signal is applied to the battery, the battery terminal voltage and current response are measured, and the battery equivalent internal resistance value is obtained through Fourier transform. During the measurement process, the battery is in a constant temperature environment of 25°C, the measurement frequency is set to 1kHz, and the sampling time is 10s. By digitally filtering and fitting the sampled data, the battery internal resistance characteristic value is obtained, which is expressed in milliohms.

[0030] In the battery operation status data acquisition process, various parameters of the battery pack are monitored in real time. The voltage of each single battery in the battery pack is sampled with a sampling frequency of 10 Hz; the total voltage value of the battery pack is synchronously collected with a collection accuracy of ±0.1 V; the charging current is monitored in real time, and the sampling frequency is synchronized with the voltage sampling with a collection accuracy of ±0.1 A. The collected data is processed by digital filtering to form a battery operation status data set, including the single battery voltage array, the total voltage value, and the charging current value.

[0031] In the Coulomb efficiency calculation process, the battery capacity attenuation is analyzed by using the battery internal resistance characteristic value and the operation status data. Based on the energy loss calculation during the battery charge and discharge process, combined with the internal resistance characteristic value, the Coulomb efficiency of a single charge and discharge cycle is calculated. By comparing the difference between the current Coulomb efficiency and the new battery standard Coulomb efficiency, the battery capacity attenuation coefficient is calculated, and this coefficient reflects the ratio of the actual battery capacity to the rated capacity.

[0032] In the battery health level determination value acquisition process, a piecewise function is used to map the capacity attenuation coefficient to the health level interval. The piecewise function mapping process plays a key role in the battery health status assessment. This process converts the battery capacity attenuation coefficient into a standardized battery health level determination value. The following is a detailed description of the mapping process: The piecewise function mapping process uses five intervals for discrete mapping, and each interval corresponds to a different battery health status. The mapping function f(x) is defined as follows: When the capacity attenuation coefficient x > 0.9, f(x) = 1.0; when 0.8 < x ≤ 0.9, f(x) = 0.8; when 0.7 < x ≤ 0.8, f(x) = 0.6; when 0.6 < x ≤ 0.7, f(x) = 0.4; when x ≤ 0.6, f(x) = 0.2.

[0033] In the actual execution process, the system reads the battery capacity attenuation coefficient x, and determines the corresponding mapping value by judging the interval where x falls. The output value range of the mapping function is [0.2, 1.0], and the larger the output value, the better the battery health status. The output value of 1.0 corresponds to the excellent state, indicating that the battery capacity retention rate is higher than 90%; the output value of 0.8 corresponds to the normal state, indicating that the battery capacity retention rate is between 80% - 90%; the output value of 0.6 corresponds to the general state, indicating that the battery capacity retention rate is between 70% - 80%; the output value of 0.4 corresponds to the poor state, indicating that the battery capacity retention rate is between 60% - 70%; the output value of 0.2 corresponds to the dangerous state, indicating that the battery capacity retention rate is lower than 60%.

[0034] The mapping process realizes the standardized conversion of battery capacity attenuation coefficient to health level judgment value. As an important indicator for battery status evaluation, health level judgment value directly affects the selection and optimization of charging strategy. The mapping result and other status parameters together constitute the initial status data of the battery, providing a quantitative judgment basis for charging process control. The state construction phase integrates the battery health level judgment value and the operating status data to generate the battery initial state data. The state data includes indicators such as the health level judgment value, single cell voltage balance, total voltage stability, and charging current fluctuation rate. The health level judgment value directly reflects the overall health of the battery; the single cell voltage balance is obtained by calculating the standard deviation of the single cell voltage, and the smaller the standard deviation, the better the balance; the total voltage stability is obtained by calculating the coefficient of variation of the total voltage; the charging current fluctuation rate is obtained by calculating the ratio of the peak-to-peak value of the charging current to the mean.

[0035] This embodiment realizes accurate evaluation of the health status of the battery by collecting and analyzing multi-dimensional parameters of the battery pack of the electric scooter. By measuring the internal resistance of the battery using the AC impedance method and combining it with real-time operating status data, the actual performance of the battery can be accurately reflected. By introducing the Coulomb efficiency calculation and piecewise function mapping mechanism, a quantitative correspondence between battery capacity decay and health level is established, making the evaluation results more objective and reliable. The constructed battery initial state data includes multiple indicators such as health level judgment value, single cell voltage balance, and total voltage stability, which provides comprehensive data support for the optimization of charging strategy. This systematic evaluation method not only improves the accuracy of battery management, but also effectively prevents abnormal conditions such as overcharging and over-discharging, prolongs the battery life, and improves the safety and reliability of electric scooters.

[0036] In one embodiment, the charger type information of the electric scooter is obtained, and matched and analyzed with the battery initial state data to obtain target charging information, including: During the charger voltage and current signal sampling and detection process, the control system samples the voltage and current signals at the charger output end based on the sampling circuit, with a sampling frequency of 1kHz and a sampling duration of 10ms, to obtain voltage sampling data sequences and current sampling data sequences. Feature extraction is performed on the sampling data sequence to extract characteristic quantities such as voltage peak value, voltage valley value, voltage effective value, current peak value, current valley value, and current effective value, and these characteristic quantities are combined into a charger characteristic parameter vector.

[0037] During the type matching process, the control system pre-stores a standard characteristic parameter library of different types of chargers, including three types of parameter data: original chargers, brand certified chargers, and non-certified chargers. By calculating the Euclidean distance between the charger characteristic parameter vector and each type of parameter in the standard characteristic parameter library, the type with the smallest distance is selected as the type information of the current charger. The characteristic parameters of the original charger are exactly the same as the factory standard charger, the characteristic parameters of the brand certified charger have an error within ±5% from the original charger, and the characteristic parameter error of the non-certified charger exceeds ±5%.

[0038] During the charging safety assessment process, the control system reads the battery temperature, voltage, remaining capacity and other status data in the battery management system as the battery initial status data. The charger type information and the battery initial status data are comprehensively analyzed, and the charging safety factor is calculated based on the preset assessment rules. The assessment rules include: the battery temperature is in the range of 0-45℃ as the safe temperature range, the battery voltage is in the range of 2.5V-4.2V as the safe voltage range, and the remaining capacity changes linearly in the range of 0-100%. When the status data are all within the safe range and the original charger is used, the safety factor is 1.0; when the status data are within the safe range and the brand certified charger is used, the safety factor is 0.8; when the status data are within the safe range and a non-certified charger is used, the safety factor is 0.6; when any status data exceeds the safe range, the safety factor is 0. The charging safety level is determined according to the value of the safety factor. The safety factor 1.0 corresponds to the safety level A, the safety factor 0.8 corresponds to the safety level B, the safety factor 0.6 corresponds to the safety level C, and the safety factor 0 corresponds to the safety level D.

[0039] During the graded analysis process, the control system sets corresponding charging limit parameters according to different safety levels. At safety level A, the maximum charging current is 100% of the nominal charging current, and the charging cut-off voltage is 4.2V; at safety level B, the maximum charging current is 80% of the nominal charging current, and the charging cut-off voltage is 4.1V; at safety level C, the maximum charging current is 60% of the nominal charging current, and the charging cut-off voltage is 4.0V; at safety level D, charging is prohibited. These limit parameters are combined into graded charging parameters.

[0040] During the target identification process, the control system matches and analyzes the graded charging parameters with the charger type information. Based on the output capacity of the charger type and the limit requirements of the graded charging parameters, the actual available charging current and charging voltage values ​​are calculated. When the output capacity of the original charger and the brand-certified charger meets the graded charging parameter requirements, the value limited by the graded charging parameters is used; when the output capacity is insufficient, the maximum output capacity value of the charger is used. Non-certified chargers are limited to the minimum charging current. The determined charging current value and charging voltage value form the target charging information.

[0041] This embodiment realizes intelligent identification of different chargers by sampling and detecting the voltage and current signals of the charger and matching the types, thus avoiding the safety hazards caused by using mismatched chargers. By conducting safety assessment and hierarchical analysis on the charger type and the initial state of the battery, a complete safety protection mechanism is established to prevent dangerous situations such as overcharging, over-discharging and abnormal temperature. The setting of graded charging parameters enables different charging strategies to be adopted under different safety levels, ensuring that the battery can obtain the optimal charging effect under various conditions. The generation process of the target charging information takes into account the balance between the actual output capacity of the charger and the safety limit requirements, making the charging process safe and efficient.

[0042] In one embodiment, a charging simulation is performed based on the target charging information, and a balanced charging control is started according to the voltage difference of the single cells to obtain charging control parameters, including: The charging method of the electric scooter includes a series of charging simulation operations based on target charging information, and starts balanced charging control according to the voltage difference of the single cells, and finally obtains the charging control parameters.

[0043] When extracting parameters of the target charging information, the preset charging curve is obtained from the charging controller, which corresponds to the charging strategy at different charging stages. In the initial stage of charging, low-rate charging is used to protect the battery; in the middle stage, higher-rate charging is used to improve charging efficiency; in the later stage, the charging rate is reduced again to extend the battery life. The charging current value is recorded every 100 milliseconds through the data acquisition module, and these data points are arranged in chronological order to form a complete charging current time series data. These data truly reflect the dynamic change characteristics of the current during the entire charging process.

[0044] When performing numerical integration calculations based on the charging current time series data, the charging process is divided into several small time periods. In each time period, the charging amount in that period is calculated, and the charging amounts of all time periods are accumulated one by one to draw the battery pack charging capacity curve. This curve shows the amount of electricity that the battery pack has received during the charging process and reflects the dynamic changes in the charging speed. The accuracy of the calculation results is ensured by a high-precision integration algorithm, and the integration accuracy is controlled within 0.01 ampere-hours.

[0045] When statistically analyzing the initial state data of the battery, the real-time voltage data of each single cell in the battery pack is collected. The voltage collection frequency is once per second, and the collection is continuous for 60 seconds to obtain stable and reliable voltage data. The collected voltage data is statistically processed to calculate key parameters such as the average voltage value, the highest voltage value, and the lowest voltage value. At the same time, the voltage data is classified and counted according to the preset interval to generate a voltage distribution diagram, which intuitively displays the distribution of the number of batteries at each voltage level in the battery pack.

[0046] When calculating the variance of the voltage distribution characteristics of the single cell, the discreteness of the voltage data is deeply analyzed. By calculating the standard deviation and coefficient of variation, the consistency level of the battery pack is quantitatively evaluated. Strict evaluation standards are set: when the voltage standard deviation exceeds 50 millivolts or the coefficient of variation exceeds 2%, it indicates that there is significant inconsistency in the battery pack, and the corresponding balancing measures will be initiated. These indicators intuitively reflect the health status and balancing requirements of the battery pack.

[0047] When setting the compensation coefficient according to the voltage deviation index, a multi-level compensation strategy is adopted. According to the severity of the voltage deviation, the compensation coefficient is divided into three levels: a small compensation coefficient is used for mild imbalance, a medium compensation coefficient is used for moderate imbalance, and a large compensation coefficient is used for severe imbalance. The setting of the compensation coefficient takes into account the actual condition of the battery pack, ensuring the balancing effect while avoiding energy loss caused by over-compensation.

[0048] When verifying the effectiveness of the equalization adjustment parameters, historical charging data is used for verification. The verification process includes two steps: simulation test and effect evaluation. In the simulation test, different compensation coefficients are used to simulate charging and record the equalization effect under various conditions. In the effect evaluation, the focus is on the two indicators of equalization time and equalization efficiency to ensure that the equalization parameters can achieve the expected effect without significantly extending the charging time.

[0049] When prioritizing the balancing trigger thresholds, a complete priority evaluation system was established. The evaluation content includes the voltage deviation degree, battery capacity status and historical balancing records. Batteries with large voltage deviations are prioritized, while considering the actual capacity level of the battery to avoid over-balancing batteries with severe capacity decay. The resulting balancing adjustment sequence reflects the optimal balancing processing order.

[0050] When calculating the compensation amount according to the equalization adjustment sequence, an accurate compensation plan is formulated for each battery to be equalized. The calculation of the compensation amount takes into account factors such as voltage difference, battery capacity and equalization efficiency. For batteries with higher voltage, the amount of electricity that needs to be released is calculated; for batteries with lower voltage, the amount of electricity that needs to be replenished is calculated. These compensation amounts constitute a detailed equalization correction plan.

[0051] When correcting the charging capacity curve based on the equalization correction value, first conduct a comprehensive analysis and preprocessing of all the equalization correction values. Sort the correction values ​​by battery position, perform secondary verification on abnormal correction values ​​that deviate from the average value by more than 30%, and perform normalization processing at the same time, converting the correction amount into milliampere-hour units.

[0052] Then, an equalizer efficiency model was established, which reflects the actual equalization efficiency under different working conditions. In the temperature range of 15-35℃, the equalization efficiency is maintained above 90%, and when it is below 15℃ or above 35℃, the efficiency will decrease linearly to a minimum of 75%. At the same time, the equalization response time parameters are introduced, including a startup delay of 100ms and a stabilization time of 500ms.

[0053] The equalization compensation effect is integrated into the original charging capacity curve according to the time series distribution, and the time period is divided into 10-second intervals using the segmented integration method. In each time period, the actual impact value of the equalization correction amount is calculated and superimposed on the capacity data. A battery state prediction model is established to predict the changes in voltage, temperature and internal resistance during the charging process. When the voltage deviation exceeds 20mV or the temperature deviation exceeds 2°C, the correction value is dynamically adjusted.

[0054] Based on the corrected capacity curve and state prediction results, specific charging control parameters are generated: Generate a current-time control curve with 500-1000 control points, calculate the voltage limit threshold of 105%-107% of the nominal voltage, set the rate adjustment coefficient to reduce 5%-10% in the equalization stage, generate a temperature-voltage compensation curve in the range of -3mV / ℃ to -5mV / ℃, and set the abnormal monitoring thresholds for voltage, temperature and equalization.

[0055] The effectiveness of the control parameters was verified through virtual charging simulation, simulating the charging process under the temperature range of -10℃ to 50℃, 10%-90% initial power and different degrees of balancing requirements. The time distribution of the control points was optimized according to the simulation results, a 5%-10% safety margin was added to the limiting parameters, and the balancing time window was adjusted.

[0056] The final output is complete charging control parameters, including: a digital charging curve with a time resolution of 10 seconds / point, a status monitoring parameter table containing all monitoring thresholds and trigger conditions, a detailed balanced start-stop strategy and resource allocation plan, and contingency plans for various abnormal situations.

[0057] This embodiment can accurately reflect the actual equalization efficiency under different temperature conditions by comprehensively analyzing and preprocessing the equalization correction value, combined with the establishment of the equalizer efficiency model, and effectively improves the accuracy of the charging process. The piecewise integration method is used to integrate the timing of the equalization compensation effect, and the real-time monitoring and dynamic adjustment of the battery state prediction model are combined to significantly enhance the adaptability and stability of the charging process. By generating a current-time curve containing multiple control points and setting a reasonable voltage limit threshold and rate adjustment coefficient, refined control of the charging process is achieved. Combined with virtual charging simulation verification and multi-dimensional parameter optimization, the reliability of the control parameters under different temperatures and initial power conditions is ensured. The complete charging control parameter set finally output realizes all-round monitoring and precise adjustment of the charging process, which not only ensures the safety and efficiency of charging, but also maximizes the battery life.

[0058] In one embodiment, numerical integration calculation is performed according to the charging current time series data to obtain a battery pack charging capacity curve, including: The charging current timing data is divided into time windows to obtain sampling sub-intervals. The charging current timing data is divided according to the preset time interval T, and the length of each time window is ΔT. The division of the time window is based on the battery charging characteristic curve, and the entire charging process is divided into n sub-intervals. Each sub-interval contains a fixed number of sampling points, which are obtained based on the sampling frequency set by the system. The divided sampling sub-intervals constitute a complete sequence from the start time of charging to the end time of charging.

[0059] The endpoint function value of the sampling sub-interval is calculated to obtain the current value of the interval boundary point. The current sequence of the boundary point is obtained by collecting the charging current value at the endpoint moment of each sub-interval. In the case where the sampling moment and the endpoint moment do not coincide, the linear interpolation method is used to calculate the boundary point current value. This method estimates the current value at the required moment through the current values ​​of two adjacent sampling points.

[0060] The current value of the interval boundary point is integrated according to the preset Simpson coefficient to obtain the initial value of the current integration. The Simpson integral formula is applied to each sub-interval, which uses the current values ​​at the starting point, midpoint and end point of the interval for weighted calculation. The Simpson coefficient [1, 4, 1] is used to weight the current values ​​of the three characteristic points of the interval to improve the integration accuracy.

[0061] Among them, the formula for calculating the initial value of current integral using the Simpson integral method is as follows: For the entire charging process, the calculation formula for the initial value of the current integral is: :This means that from time arrive The integral of the current I(t) over time.

[0062] h: half of the subinterval length, that is ( - ) / 2.

[0063] , , : are the current values ​​at the starting point, midpoint and end point of the sub-interval respectively.

[0064] : Indicates the initial value of the current integral of the entire charging process.

[0065] Σ: summation symbol, indicating the accumulation of calculation results of all subintervals.

[0066] i=0 to n-2: means accumulation from the first subinterval to the second to last subinterval.

[0067] ( - ) / 6: This is 1 / 6 of the subinterval length and is used to normalize the calculation results.

[0068] The initial value of the current integral is corrected by the interval step length to obtain the corrected capacity increment. The battery charging efficiency coefficient and temperature correction coefficient are introduced into the correction process. When the battery temperature exceeds the preset threshold, the correction coefficient will decrease as the temperature rises, thereby reflecting the impact of temperature on charging efficiency. The capacity increment of each sub-interval is corrected accordingly.

[0069] The cumulative capacity sequence is obtained by adding up the capacity increments. Taking the capacity at the start of charging as the benchmark, the capacity cumulative sequence reflecting the charging process is obtained by gradually adding up the corrected capacity increments of each time interval. This sequence shows the growth of battery capacity over time.

[0070] The accumulated capacity sequence is processed by Kalman filtering to obtain the state estimation result. The Kalman filter optimizes the capacity sequence through the state equation and the measurement equation. The filter parameters include the state transfer matrix, the measurement matrix, the process noise covariance and the measurement noise covariance. A smoother and more accurate state estimation sequence is obtained through filtering.

[0071] The adaptive control model is constructed based on the state estimation results, and dynamic capacity planning optimization is performed to obtain the target capacity state information. The adaptive control model consists of three core parts: state predictor, evaluator, and controller. The model can predict future capacity changes based on the current state, evaluate the prediction accuracy, and adjust the charging strategy accordingly. The optimization process comprehensively considers the charging speed and battery health status to obtain the optimal target capacity state sequence.

[0072] The charging capacity curve of the battery pack is obtained by curve mapping based on the target capacity state information. The process first normalizes the target capacity state sequence, and then maps the normalized result to the actual rated capacity range of the battery. The final charging capacity curve fully reflects the capacity change law of the battery during the charging process, providing an important basis for charging control and battery management.

[0073] This embodiment achieves accurate estimation and optimized control of the charging capacity of the battery pack by finely processing and analyzing the charging current time series data. The accuracy of capacity calculation is improved by time window division and Simpson integration. The introduction of the temperature correction coefficient effectively considers the influence of temperature on charging efficiency, making the capacity estimation closer to the actual situation. The cumulative capacity sequence is processed by Kalman filtering technology, which effectively reduces the influence of measurement noise and improves the stability and reliability of capacity estimation. The introduction of the adaptive control model enables the system to dynamically adjust the charging strategy according to the current state and strike a balance between charging speed and battery health. The charging process is optimized and it also helps to extend the battery life. The final battery pack charging capacity curve provides an accurate reference basis for the battery management system, which is conducive to realizing intelligent and personalized charging control and improving battery efficiency and safety.

[0074] In one embodiment, a charging protection threshold is set according to the charging control parameter, and a strategy is constructed with the target charging information to obtain a charging protection strategy, including: The threshold analysis of the charging control parameters is performed. Specifically, by measuring the current voltage and current data and comparing them with the preset safety range, the charging voltage threshold and charging current threshold are obtained. These thresholds are key data to ensure that the battery pack remains within the safe operating range during the charging process.

[0075] After obtaining the charging voltage threshold and charging current threshold, the charging state analysis is performed based on these thresholds to obtain the battery pack charging state data. The charging state analysis includes real-time monitoring of battery voltage, current, temperature and other parameters to determine the current charging state of the battery. The purpose of this step is to be able to understand the health status of the battery in real time so as to make corresponding adjustments.

[0076] Perform safety parameter analysis on the battery pack charging status data and target charging information to extract charging safety parameters. These safety parameters include but are not limited to the battery's upper temperature limit, overcharge protection voltage, overcurrent protection current, etc. Through these analyses, ensure that all battery parameters are within a safe range during the charging process to avoid battery damage or safety accidents caused by overheating, overvoltage or overcurrent.

[0077] According to the charging safety parameters, the charging process is dynamically adjusted to obtain the charging adjustment strategy. In this process, the charging voltage and current are adjusted in real time to ensure that the charging process is carried out within a safe and effective range. For example, when the battery temperature is too high, the charging current will be automatically reduced, or when the battery voltage is close to the upper limit, the charging amount will be gradually reduced to avoid overcharging.

[0078] Threshold extraction is performed on the charging regulation strategy to obtain the charging protection threshold range. The main purpose of this step is to extract specific protection thresholds based on the actual detected charging status, so that real-time protection can be performed based on these thresholds during the subsequent charging process. For example, when the battery voltage reaches a certain critical value, charging will be automatically stopped, or when the battery temperature is detected to exceed a certain threshold, heat dissipation measures will be automatically started.

[0079] According to the charging protection threshold range, the target charging information is graded and processed to generate graded charging protection data. The purpose of graded processing is to classify different charging states, such as normal charging, fast charging, slow charging, etc., to ensure that there are corresponding protection measures in different states. The result of this step is to generate a series of graded charging protection data so that in the actual charging process, corresponding protection strategies can be adopted according to different charging states.

[0080] According to the preset strategy database, the charging strategy is matched to the hierarchical charging protection data to obtain the initial protection strategy. The strategy database stores the preset values ​​and corresponding conditions of various charging protection strategies. By matching the current hierarchical charging protection data, the most suitable charging protection strategy is found. These strategies include adjusting the charging current and voltage, or stopping charging when necessary to ensure the safety and efficiency of the charging process.

[0081] The charging control parameters of the initial protection strategy are configured to obtain the charging protection strategy. By configuring the various parameters in the initial protection strategy in detail, a complete charging protection strategy is finally formed. These parameter configurations include the specific values ​​of the charging voltage and current, the specific range of temperature protection, etc. Through these configurations, it is ensured that the entire charging process can be adjusted automatically and in real time to avoid any possible safety hazards.

[0082] This embodiment can monitor the battery charging status of the electric scooter in real time by setting the charging protection threshold based on the charging control parameters, ensuring that the battery is always within the safe operating range during the charging process. Through threshold analysis and safety parameter analysis, the system can dynamically adjust the charging current and voltage to effectively prevent overcharging, overcurrent or overheating, thereby extending the battery life and improving the battery charging efficiency. Through the generation of hierarchical charging protection data, the charging strategy can be accurately adjusted according to different charging states to ensure that each charge is carried out under optimal conditions to avoid damage to the battery. In addition, the charging strategy is matched in combination with the preset strategy database, so that the safety and efficiency of the charging process are maximized, reducing unnecessary energy waste and loss. Overall, this embodiment achieves high efficiency, safety and energy saving in the charging process through precise charging control and intelligent dynamic adjustment.

[0083] In one embodiment, the electric scooter is charged and controlled based on the charging protection strategy, and charging identification is performed to obtain a charging record, including: When parsing the charging protection strategy, the system first receives the preset charging protection strategy, which may include the maximum charging voltage, maximum charging current, temperature conditions, etc. of the battery. The system generates a charging control instruction set by parsing these strategies. These instruction sets contain specific requirements for charging the electric scooter, for example, when the battery voltage reaches a certain value, the charging current should be reduced.

[0084] When the charging of the electric scooter is segmented according to the charging control instruction set, the system divides the entire charging process into multiple stages, each with different charging parameters. For example, the initial stage may be charged with a constant current, and when the battery voltage reaches a certain critical value, it switches to a constant voltage charging mode. This segmented control can effectively protect the battery and extend its service life. During the charging process, the system will record the charging data of each stage and obtain segmented charging data.

[0085] When monitoring the segmented charging data in real time, the system continuously collects data generated during the charging process, such as current, voltage, temperature, etc., through embedded sensors and monitoring modules. The system monitors and analyzes these data in real time to obtain the current charging status information. This step ensures that the charging process is carried out within a safe and effective range to avoid problems such as overcharging and overheating.

[0086] When sampling the voltage of the electric scooter according to the charging status information, the system analyzes the current charging status and samples the battery voltage at the appropriate time. The voltage sampling data reflects the specific voltage changes of the battery during the charging process, which is of great significance for subsequent charging control and adjustment.

[0087] When performing difference calculation on voltage sampling data, the system analyzes the collected voltage data and calculates the voltage difference between different time points to obtain voltage balancing parameters. Voltage balancing parameters are used to evaluate the voltage consistency of each battery cell to ensure that each cell can be evenly charged during the charging process to avoid overcharging or undercharging of some cells.

[0088] When dynamically adjusting the charging current of an electric scooter according to the voltage equalization parameters, the system dynamically adjusts the charging current value based on the calculated voltage equalization parameters. The purpose of this step is to balance the voltage of each battery cell to make the charging process more uniform and safe. For example, when the voltage of some cells is too high, the system will reduce the charging current to prevent these cells from overcharging.

[0089] When charging is controlled by adjusting the charging current value, the system applies the dynamically adjusted charging current value to the actual charging process and records real-time charging data. These data include the current charging current, voltage, power, etc., reflecting the instantaneous status of the charging process.

[0090] When performing feature extraction processing based on real-time charging data, the system analyzes the recorded real-time charging data and extracts charging feature values. These feature values ​​may include charging time, charging efficiency, battery temperature changes, etc., reflecting the specific characteristics and performance of the entire charging process.

[0091] When identifying and classifying charging characteristic values, the system identifies and classifies the extracted charging characteristic values ​​according to the preset classification standards. The charging type identification reflects the different types of charging processes, such as fast charging, slow charging, balanced charging, etc.

[0092] When monitoring the charging of an electric scooter based on the charging type identification, the system monitors and adjusts the subsequent charging process in a targeted manner based on the identified charging type. For example, for fast charging types, the system may pay more attention to changes in battery temperature to avoid overheating problems. In this way, the system can control the charging process more accurately to ensure charging safety and efficiency.

[0093] Finally, all data and analysis results of the entire charging process will be recorded in the charging record. These records include charging data at each stage, charging status information, voltage sampling data, voltage equalization parameters, adjusted charging current values, real-time charging data, charging characteristic values, and charging type identification. Charging records can provide important reference for subsequent maintenance, analysis, and improvement.

[0094] This embodiment implements refined management of the charging process by performing instruction parsing and segmented control on the charging protection strategy of the electric scooter charging method, effectively avoiding the problems of overcharging and undercharging of the battery. Real-time monitoring and voltage sampling of segmented charging data, combined with differential calculation of voltage balancing parameters, enable the system to promptly detect abnormal conditions during the charging process, ensuring the safety and stability of battery charging. The dynamic adjustment mechanism of the charging current, combined with the feature extraction and identification classification of real-time charging data, not only improves the charging efficiency, but also extends the service life of the battery. Through intelligent identification and monitoring of charging types, the system can select the optimal charging strategy for different charging scenarios, greatly improving the adaptability and intelligence level of the charging process.

[0095] Reference Figure 2 As shown, the present invention also provides a charging system for an electric scooter, which is applied to any of the above-mentioned charging methods for an electric scooter, comprising: The acquisition module is used to obtain the battery pack parameters of the electric scooter, analyze the battery health status, and obtain the battery initial status data; An analysis module is used to obtain the charger type information of the electric scooter, match and analyze it with the initial state data of the battery, and obtain target charging information; The association module is used to perform charging simulation based on the target charging information, and start the balanced charging control according to the voltage difference of the single cells to obtain the charging control parameters; A processing module, the processing module is used to set a charging protection threshold according to the charging control parameter, and construct a strategy with the target charging information to obtain a charging protection strategy; The control module is used to control the charging of the electric scooter based on the charging protection strategy, and to perform charging identification to obtain charging records.

[0096] The present invention provides a charging system for an electric scooter. By acquiring battery pack parameters and performing battery health status analysis, the working status and performance indicators of the battery can be grasped in real time, providing a reliable basis for formulating a reasonable charging strategy, and effectively avoiding the battery loss problem caused by a simple charging method. By combining the charger type information with the battery initial state data for matching analysis, the charging parameters can be optimized according to the characteristics of different chargers to ensure the safety and efficiency of the charging process, and overcome the defect that the traditional charging method does not consider the characteristics of the charger enough. Through charging simulation and balanced charging control, the voltage difference problem between the single cells can be effectively solved, the occurrence of local overcharge or over-discharge can be avoided, and the service life of the battery pack can be extended. The charging protection threshold is set according to the charging control parameters and the charging protection strategy is constructed to achieve all-round protection of the charging process, effectively prevent the battery from being damaged by improper charging, and improve the safety of the electric scooter. Charging control and identification based on the charging protection strategy can not only ensure the safety and reliability of the charging process, but also generate detailed charging records, which is convenient for users to understand the battery usage status and charging efficiency, and provide data support for the daily maintenance and management of the electric scooter. Through the intelligent charging control system, refined management of the battery charging process is achieved, which improves the charging efficiency and extends the battery life, meeting the user's demand for long-term and stable use of electric scooters.

[0097] Reference Figure 3 As shown, the present invention also provides a charging device for an electric scooter, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned methods for charging an electric scooter.

[0098] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0099] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0100] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0101] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for charging an electric scooter, characterized in that: include: Obtain the battery pack parameters of the electric scooter, perform battery health status analysis, and obtain the battery initial status data; Obtaining charger type information of the electric scooter, and performing matching analysis with the battery initial state data to obtain target charging information; Performing charging simulation based on the target charging information, and starting balanced charging control according to the voltage difference of the single cells to obtain charging control parameters; Setting a charging protection threshold according to the charging control parameter, and constructing a strategy with the target charging information to obtain a charging protection strategy; Based on the charging protection strategy, the electric scooter is charged and identified to obtain a charging record.

2. The method for charging an electric scooter according to claim 1, characterized in that: The method of obtaining the battery pack parameters of the electric scooter and performing battery health status analysis to obtain the battery initial status data includes: Identify the battery pack of the electric scooter to obtain the battery pack parameters including the number of charge and discharge times and the usage time; Measuring the battery internal resistance of the electric scooter according to the battery pack parameters to obtain a battery internal resistance characteristic value; Real-time sampling of the battery cell voltage, total voltage and charging current of the electric scooter to obtain battery operating status data; Calculate the coulomb efficiency according to the battery internal resistance characteristic value and the battery operating status data to obtain the battery capacity attenuation coefficient; Performing piecewise function mapping on the battery capacity attenuation coefficient to obtain a battery health level determination value; The state is constructed according to the battery health level determination value and the battery operation state data to obtain the battery initial state data.

3. The method for charging an electric scooter according to claim 1, characterized in that: The acquiring of the charger type information of the electric scooter and matching analysis with the battery initial state data to obtain target charging information includes: Sampling and detecting the voltage and current signals of the charger of the electric scooter to obtain characteristic parameters of the charger; Performing type matching on the characteristic parameters of the charger according to preset type data to obtain the charger type information; Performing a charging safety assessment on the charger type information and the battery initial state data to obtain a charging safety level; Performing a hierarchical analysis on the charging safety level to obtain a hierarchical charging parameter; Target identification is performed according to the graded charging parameters and the charger type information to obtain the target charging information.

4. The method for charging an electric scooter according to claim 1, characterized in that: The charging simulation is performed based on the target charging information, and balanced charging control is started according to the voltage difference of the single cells to obtain charging control parameters, including: Extracting parameters of the target charging information to obtain charging current time series data; Performing numerical integration calculation according to the charging current time series data to obtain a charging capacity curve of the battery pack; Performing statistical analysis on the battery initial state data to obtain single cell battery voltage distribution characteristics; Calculating the index variance of the voltage distribution characteristics of the single cell to obtain a voltage deviation index; A compensation coefficient is set according to the voltage deviation index to obtain a balance adjustment parameter; Verifying the validity of the balance adjustment parameter to obtain a balance trigger threshold; Prioritizing the equalization triggering thresholds to obtain an equalization adjustment sequence; Calculate the compensation amount according to the balance adjustment sequence to obtain a balance correction value; The charging capacity curve is corrected according to the equalization correction value to obtain a charging control parameter.

5. The method for charging an electric scooter according to claim 4, characterized in that: The step of performing numerical integration calculation according to the charging current time series data to obtain a charging capacity curve of the battery pack includes: Dividing the charging current time series data into time windows to obtain sampling sub-intervals; Calculating the endpoint function value of the sampling sub-interval to obtain the current value of the interval boundary point; Performing current integration on the current value at the boundary point of the interval according to a preset Simpson coefficient to obtain an initial value of current integration; Performing interval step correction on the initial value of the current integration to obtain a corrected capacity increment; Performing accumulation operation according to the capacity increment to obtain a cumulative capacity sequence; Performing Kalman filtering on the accumulated capacity sequence to obtain a state estimation result; An adaptive control model is constructed according to the state estimation result, and dynamic capacity planning optimization is performed to obtain target capacity state information; Curve mapping is performed according to the target capacity state information to obtain the battery pack charging capacity curve.

6. The method for charging an electric scooter according to claim 1, characterized in that: The step of setting a charging protection threshold according to the charging control parameter and constructing a strategy with the target charging information to obtain a charging protection strategy includes: Performing threshold analysis on the charging control parameter to obtain a charging voltage threshold and a charging current threshold; Perform charging state analysis according to the charging voltage threshold and the charging current threshold to obtain battery pack charging state data; Performing safety parameter analysis on the battery pack charging state data and the target charging information to obtain charging safety parameters; Dynamically adjust the charging process according to the charging safety parameters to obtain a charging adjustment strategy; Performing threshold extraction on the charging regulation strategy to obtain a charging protection threshold interval; hierarchically processing the target charging information according to the charging protection threshold interval to obtain hierarchical charging protection data; Performing charging strategy matching on the hierarchical charging protection data according to a preset strategy database to obtain an initial protection strategy; The charging control parameters of the initial protection strategy are configured to obtain a charging protection strategy.

7. The method for charging an electric scooter according to claim 1, characterized in that: The charging control of the electric scooter based on the charging protection strategy and the charging identification to obtain the charging record include: Performing instruction parsing on the charging protection strategy to obtain a charging control instruction set; Performing segmented charging control on the electric scooter according to the charging control instruction set to obtain segmented charging data; Performing real-time monitoring on the segmented charging data to obtain charging status information; Performing voltage sampling on the electric scooter according to the charging status information to obtain voltage sampling data; Performing difference calculation on the voltage sampling data to obtain voltage balancing parameters; Dynamically adjusting the charging current of the electric scooter according to the voltage equalization parameter to obtain an adjusted charging current value; Performing charging control on the adjusted charging current value to obtain real-time charging data; Performing feature extraction processing according to the real-time charging data to obtain a charging feature value; Identify and classify the charging characteristic values ​​to obtain a charging type identifier; The charging of the electric scooter is monitored according to the charging type identifier to obtain the charging record.

8. A charging system for an electric scooter, characterized in that: The charging method for the electric scooter according to any one of claims 1 to 7 comprises: A collection module, which is used to obtain battery pack parameters of the electric scooter and perform battery health status analysis to obtain battery initial status data; An analysis module, the analysis module is used to obtain the charger type information of the electric scooter, and perform matching analysis with the battery initial state data to obtain target charging information; An association module, the association module is used to perform charging simulation based on the target charging information, and start balanced charging control according to the voltage difference of the single cells to obtain charging control parameters; A processing module, the processing module is used to set a charging protection threshold according to the charging control parameter, and construct a strategy with the target charging information to obtain a charging protection strategy; A control module is used to control the charging of the electric scooter based on the charging protection strategy, and perform charging identification to obtain a charging record.

9. A charging device for an electric scooter, characterized in that: include: Memory, used to store programs; The processor is used to execute the program to implement the various steps of the charging method for an electric scooter as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 7.

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