Electric vehicle charging management system and method based on low temperature compensation

Abstract

The invention discloses an electric vehicle charging management system and method based on low temperature compensation, and relates to the technical field of electric vehicle charging management, and the method specifically comprises the following steps: obtaining the battery feedback information of a portable charging pile at the initial charging stage in real time, and respectively generating a temperature lag coefficient and an internal resistance adaptation index; and constructing an initial current setting evaluation model for the generated temperature lag coefficient and the internal resistance adaptation index, generating an initial current evaluation coefficient, and evaluating the demand matching degree of the battery to the initial charging current in a low-temperature environment. According to an evaluation result, the three conditions are divided into charging current complete matching, charging current insufficient matching and charging current excessive matching; and respectively taking corresponding measures according to the division result of the initial charging current demand matching degree. According to the invention, the problem of inaccurate current setting caused by battery charging feedback lag in a low-temperature environment is solved, and the efficient charging effect of dynamic matching current is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging management, and in particular to an electric vehicle charging management system and method based on low temperature compensation. Background Art

[0002] Electric vehicle charging refers to the transmission of electrical energy to the battery pack of an electric vehicle through a charging device to maintain the vehicle's endurance. Since the battery is affected by many factors during the charging process, such as temperature, current, voltage, etc., in order to ensure the safety and efficiency of the charging process and avoid problems such as overcharging, undercharging and overheating that shorten the battery life and even safety hazards, it is crucial to carefully manage the charging of electric vehicles. However, in a low temperature environment, the electrochemical reaction rate of the battery decreases and the internal resistance increases, which leads to reduced charging efficiency and may cause problems such as lithium metal deposition, damaging the battery life and performance. Therefore, it is necessary to introduce a low temperature compensation mechanism to dynamically adjust the charging parameters, or start the heating system to increase the battery temperature, in order to ensure the safety and charging speed of the charging process, while extending the battery life, which is particularly important for the normal use of electric vehicles in cold areas.

[0003] The existing electric vehicle charging management technology based on low-temperature compensation monitors the battery temperature in real time and automatically adjusts the charging parameters, such as voltage and current, in combination with the ambient temperature. In a low-temperature environment, a small current pre-charging method is preferred, and the charging current and voltage are gradually increased to avoid the deposition of lithium ions at the negative electrode to form metallic lithium due to high-current charging, thereby damaging the battery; at the same time, when the temperature is too low, the system starts the heating function to heat the battery first so that it enters a temperature range suitable for charging, ensuring the safety and efficiency of the charging process; when the battery temperature rises to an appropriate range during heating or charging, the system will gradually adjust to the standard constant current and constant voltage charging mode, thereby extending the battery life while ensuring charging efficiency, and through multiple voltage, temperature and current protection mechanisms, charging safety and intelligent management are achieved under low temperature conditions.

[0004] The prior art has the following deficiencies:

[0005] In an outdoor parking lot in the early morning of winter, when an electric car is recharged through a portable charging pile, the portable charging device has a slow feedback speed on the battery temperature and internal resistance in a low temperature environment, and the system cannot capture the actual demand of the battery in time, resulting in a low initial charging current setting, which leads to the problem of insufficient current pre-adjustment. The existing electric vehicle charging management technology based on low temperature compensation fails to fully consider the lag of sensor feedback of portable charging devices at low temperatures, so the charging activation speed is slow, and the battery takes longer to enter a suitable charging state, resulting in a longer charging time; this situation increases the user's waiting time when the portable charging efficiency is already low, significantly affecting the charging experience and delaying travel arrangements.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The object of the present invention is to provide an electric vehicle charging management system and method based on low temperature compensation to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: an electric vehicle charging management method based on low temperature compensation, specifically comprising the following steps:

[0009] In a low temperature environment in the early morning of winter, when an electric vehicle is charged through a portable charging pile, the initial low temperature demand information of the battery is obtained, and an initial current setting analysis framework for the battery demand under low temperature conditions is established based on the obtained initial low temperature demand information of the battery;

[0010] Real-time acquisition of battery feedback information at the initial stage of charging of a portable charging pile, and analysis after acquisition to generate a temperature hysteresis coefficient and an internal resistance adaptation index;

[0011] An initial current setting evaluation model is constructed for the generated temperature hysteresis coefficient and internal resistance adaptation index to generate an initial current evaluation coefficient, and an analysis is performed after the generation to evaluate the matching degree of the battery's initial charging current requirements in a low temperature environment, and the evaluation results are divided into three situations: complete charging current matching, insufficient charging current matching, and excessive charging current matching;

[0012] According to the division results of the matching degree of the initial charging current demand, corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures are taken respectively;

[0013] Continuously monitor battery status information during the charging process and dynamically update the initial current setting evaluation model based on real-time analysis results to ensure the safety and efficiency of the charging process under low temperature conditions.

[0014] Preferably, the battery feedback information of the portable charging pile at the initial stage of charging is obtained in real time, and analyzed after acquisition to generate a temperature hysteresis coefficient and an internal resistance adaptation index, respectively, which specifically includes the following steps:

[0015] Real-time acquisition of battery feedback information at the initial stage of charging of portable charging piles, and pre-processing after acquisition;

[0016] Extracting temperature response information and internal resistance adaptation information from pre-processed battery feedback information of the portable charging pile at the initial stage of charging;

[0017] The extracted temperature response information and internal resistance adaptation information are analyzed to generate the temperature hysteresis coefficient and internal resistance adaptation index respectively.

[0018] Preferably, the acquisition logic of the temperature hysteresis coefficient is as follows:

[0019] Extract the temperature response information from the preprocessed battery feedback information at the initial stage of charging of the portable charging pile, specifically including the actual temperature of the battery surface and the actual temperature of the environment at different times during a period of time during the initial stage of charging of the portable charging pile, and represent them respectively with functions TB(t) and TE(t) according to the time series, where t is the time point, and the time period is defined as [t1, t2]. TB(t) represents the actual temperature of the battery surface at time t during a period of time during the initial stage of charging of the portable charging pile, and TE(t) represents the actual temperature of the environment at time t during a period of time during the initial stage of charging of the portable charging pile;

[0020] Calculate the temperature hysteresis coefficient. The specific calculation formula is as follows:

[0021]

[0022] Where TLC is the temperature hysteresis coefficient.

[0023] Preferably, the logic for obtaining the internal resistance adaptation index is as follows:

[0024] Extract the internal resistance adaptation information from the pre-processed battery feedback information of the portable charging pile at the initial stage of charging, including the actual battery voltage, actual internal resistance and initial current value at different times during the initial period of charging of the portable charging pile, and calibrate them as VB m , RB m and IO m ,VB m Indicates the actual voltage of the battery at time m during the initial period of charging at the portable charging station, RB m It represents the actual internal resistance of the battery at time m during the initial period of charging at the portable charging station, IO mrepresents the initial current value at time m during the initial period of charging of the portable charging pile, m = 1, 2, 3, ..., g, g is a positive integer;

[0025] Calculate the internal resistance adaptation index. The specific calculation formula is as follows:

[0026]

[0027] Where IRAI is the internal resistance adaptation index.

[0028] Preferably, constructing an initial current setting evaluation model for the generated temperature hysteresis coefficient and internal resistance adaptation index to generate an initial current evaluation coefficient specifically includes the following steps:

[0029] Collect several temperature hysteresis coefficients, internal resistance adaptation indexes and corresponding initial current evaluation coefficients generated in the past period of time, and calibrate them as TLC x IRAI x and ICEC x , x represents the number of temperature hysteresis coefficients, internal resistance adaptation indexes and corresponding initial current evaluation coefficients generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set;

[0030] Select the multivariate regression model as the initial current setting evaluation model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0031] ICEC x =β0+β1*TLC x -β2*IRAI x

[0032] Where β0, β1 and β2 are regression coefficients;

[0033] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined;

[0034] The final determined regression coefficient is used to set the constructed initial current evaluation model, and the real-time generated temperature hysteresis coefficient TLC and internal resistance adaptation index IRAI are input to generate the initial current evaluation coefficient ICEC in real time.

[0035] Preferably, the generated initial current evaluation coefficient ICEC is compared with a preset initial current evaluation coefficient threshold interval [ICEC min , ICEC max] to compare, and evaluate the matching degree of the battery's initial charging current requirements in a low temperature environment according to the comparison results, and divide it into three situations according to the evaluation results: complete charging current matching, insufficient charging current matching, and excessive charging current matching. The specific comparison analysis and division are as follows:

[0036] If ICEC <ICEC min , the current initial charging current is insufficient to match the battery's demand in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is insufficiently matched with the charging current;

[0037] If ICEC min ≤ICEC≤ICEC max , the current initial charging current fully matches the battery's demand for the initial charging current in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is fully matched to the charging current;

[0038] If ICEC>ICEC max , the current initial charging current overmatches the battery's demand in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is overmatched.

[0039] Preferably, according to the division result of the matching degree of the initial charging current demand, corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures are respectively adopted, specifically:

[0040] For the case where the classification result is insufficient charging current matching, the specific measures taken are: increasing the set value of the initial charging current to increase the activation speed of the battery in a low temperature environment so that it can achieve the required charging efficiency;

[0041] When the division result is that the charging current is completely matched, the specific measures taken are: maintain the current set value of the initial charging current without further adjustment, and continuously monitor the battery status to ensure the stability of the matching degree;

[0042] For the case where the classification result is excessive charging current matching, the specific measures taken are: reducing the set value of the initial charging current to prevent battery overheating and overcharging, thereby ensuring the safety of the charging process and the service life of the battery.

[0043] Preferably, the electric vehicle charging management system based on low temperature compensation includes an initial demand analysis module, a feedback analysis generation module, an evaluation model construction module, an intelligent current regulation module and a dynamic monitoring update module;

[0044] The initial demand analysis module obtains the initial low-temperature demand information of the battery when the electric vehicle is charged by a portable charging pile in the low-temperature environment in the early morning of winter, and establishes an initial current setting analysis framework for the battery demand under low-temperature conditions based on the obtained initial low-temperature demand information of the battery;

[0045] The feedback analysis generation module obtains the battery feedback information of the portable charging pile in real time at the initial stage of charging, and analyzes it after acquisition to generate the temperature hysteresis coefficient and internal resistance adaptation index respectively;

[0046] The evaluation model building module builds an initial current setting evaluation model for the generated temperature hysteresis coefficient and internal resistance adaptation index, generates an initial current evaluation coefficient, and analyzes it after generation to evaluate the battery's demand matching degree for the initial charging current in a low temperature environment, and divides it into three situations according to the evaluation results: complete charging current matching, insufficient charging current matching, and excessive charging current matching;

[0047] The intelligent current regulation module takes corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures according to the division results of the matching degree of the initial charging current demand;

[0048] The dynamic monitoring update module continuously monitors the battery status information during the charging process and dynamically updates the initial current setting evaluation model based on the real-time analysis results to ensure the safety and efficiency of the charging process under low temperature conditions.

[0049] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0050] 1. The present invention realizes precise dynamic control of electric vehicle charging management in a low-temperature environment, and has significant technical effects and advantages. First, by acquiring the initial demand information of the battery and the feedback data of the portable charging pile in real time, the present invention can quickly evaluate the status of the battery at the beginning of charging. This real-time analysis mechanism enables the system to obtain core parameters such as the temperature hysteresis coefficient and the internal resistance adaptation index in a timely manner, overcoming the problem of slow sensor feedback speed under low temperature conditions, thereby ensuring that the initial charging current setting is more in line with the actual needs of the battery. This precisely matched charging current setting not only reduces the waiting time during the charging process, but also improves the charging efficiency of the battery in a low-temperature environment.

[0051] 2. The present invention establishes an initial current setting evaluation model, generates an initial current evaluation coefficient, and divides the matching degree into three situations: insufficient, complete, and excessive, so that the system has higher flexibility in current setting. According to the division results, the system can automatically take increasing, decreasing, or maintaining measures to accurately adjust the current output to meet the low-temperature charging requirements of the battery. This dynamic adjustment function based on actual needs fully considers the charging activation requirements of the battery under low temperature conditions, avoids charging delays, overheating, and other problems caused by insufficient or excessive current settings, thereby optimizing charging safety and battery life.

[0052] 3. The continuous monitoring and dynamic update module in the present invention ensures the efficiency and safety of the charging process. During the charging process, the system will monitor the battery status information in real time and dynamically update the evaluation model according to the data changes to adapt to the fluctuation of the battery status in a low temperature environment. The dynamic update mechanism ensures the real-time setting of the current, so that the charging current can be intelligently adjusted with changes in the environment and battery status. This feature not only effectively shortens the charging activation time at low temperatures, but also reduces the risk of loss during the charging process, improves the responsiveness, adaptability and charging experience of the charging system in a low temperature environment, and greatly improves the user's charging experience under low temperature conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is a flow chart of an electric vehicle charging management system and method based on low temperature compensation of the present invention.

[0055] Figure 2 It is a module schematic diagram of the electric vehicle charging management system and method based on low temperature compensation of the present invention. DETAILED DESCRIPTION

[0056] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0057] The present invention provides Figure 1 The electric vehicle charging management method based on low temperature compensation shown in the figure specifically includes the following steps:

[0058] In a low temperature environment in the early morning of winter, when an electric vehicle is charged through a portable charging pile, the initial low temperature demand information of the battery is obtained, and an initial current setting analysis framework for the battery demand under low temperature conditions is established based on the obtained initial low temperature demand information of the battery;

[0059] In the low temperature environment in the early morning of winter, when the electric vehicle is charged through a portable charging pile, the system can collect data from multiple sensors through a software program to obtain the initial low temperature demand information of the battery. These sensors include ambient temperature sensors, battery temperature sensors, and internal resistance detection modules. The system first obtains specific data of the external low temperature environment from the ambient temperature sensor to determine the potential impact of low temperature on the battery. At the same time, the battery temperature sensor monitors the temperature state of the battery itself in real time to understand whether the actual temperature of the battery is significantly lower than the working requirement. In addition, the internal resistance data provided by the internal resistance detection module helps the system determine the current discharge state of the battery and its ability to withstand current. All data is processed in real time by the software, and integrated to generate "initial low temperature demand information" about the battery to accurately reflect the activation requirements of the battery at the current low temperature, providing a basis for the subsequent setting of the charging current.

[0060] After obtaining the initial low-temperature demand information of the battery, the system can establish an "initial current setting analysis framework" through an algorithm based on multi-parameter analysis. The establishment of this framework first requires the algorithm to analyze the aforementioned initial battery demand information to determine the battery's sensitivity to charging current at low temperatures. This framework can process ambient temperature, internal resistance, and battery temperature data in layers to dynamically match current setting requirements. For example, the system will generate a "current setting recommendation" based on the ambient temperature and internal resistance values, and calculate a "preset initial current" suitable for low-temperature conditions based on the battery temperature data. Next, the analysis framework will compare this preset current with the actual demand data in real time, and continuously update the setting model so that the initial charging current set under low-temperature conditions is more in line with the battery's charging requirements. The entire analysis framework is dynamically updated through the interactive calculation of these data, providing a flexible prediction standard for the setting of the actual charging current.

[0061] The reason why we need to obtain the initial low-temperature demand information of the battery and establish an initial current setting analysis framework at the beginning of charging is that in a low-temperature environment, the battery's temperature and internal resistance are extremely sensitive to the matching degree of the charging current demand, and the sensor feedback of portable charging piles often has a lag. Our technical solution aims to solve the problem of low initial current setting due to feedback lag in low-temperature environments, thereby speeding up charging activation. Based on the acquisition of initial low-temperature demand information, the analysis framework can quickly evaluate the current demand of the battery and ensure that the current setting at the beginning of charging is sufficiently suitable for the actual situation of the battery. This process can shorten the time it takes for the battery to enter the activation state under low-temperature conditions, thereby avoiding charging delays, ensuring charging efficiency, improving the user's charging experience and reducing the waiting time caused by low current settings, while optimizing the safety and efficiency of the charging process under low-temperature conditions.

[0062] Real-time acquisition of battery feedback information at the initial stage of charging of a portable charging pile, and analysis after acquisition to generate a temperature hysteresis coefficient and an internal resistance adaptation index;

[0063] In this embodiment, the battery feedback information of the portable charging pile at the initial stage of charging is obtained in real time, and analyzed after acquisition to generate the temperature hysteresis coefficient and the internal resistance adaptation index respectively, which specifically includes the following steps:

[0064] Real-time acquisition of battery feedback information at the initial stage of charging of portable charging piles, and pre-processing after acquisition;

[0065] The sensor module of the portable charging pile can be used to obtain the battery feedback information in the early stage of charging in real time. Specifically, the charging system monitors the sensor data stream of the battery in real time through embedded software, and collects feedback information such as ambient temperature, battery temperature, battery voltage, initial current and internal resistance. These data are uploaded to the system by the sensor of the charging pile within a fixed sampling period of every few milliseconds. The system samples and stores the data through a high-speed data acquisition module, so that the feedback information can be captured in real time throughout the initial charging process. After data collection, it will be synchronously transmitted to the main control system for further analysis and processing.

[0066] The purpose of preprocessing is to ensure that the acquired data is accurate and standardized so that subsequent analysis and parameter calculation are more reliable. The acquired data often carries sensor noise or measurement errors, so preprocessing includes steps such as denoising, filtering, outlier detection, and data smoothing. Specifically, the system first denoises the data stream, applies a software filtering algorithm (such as low-pass filtering) to remove high-frequency interference, and then uses an outlier detection algorithm to screen abnormal data points, such as temperature or voltage readings that exceed a reasonable range; once an abnormality is detected, the system automatically discards the data point or uses interpolation to smooth the data. Finally, the data smoothing algorithm smoothes the sampled data to ensure the coherence of the data curve and reduce mutations. The data after preprocessing is consistent and reliable, providing a high-quality data basis for the subsequent generation of the temperature hysteresis coefficient and the internal resistance adaptation index.

[0067] Extracting temperature response information and internal resistance adaptation information from pre-processed battery feedback information of the portable charging pile at the initial stage of charging;

[0068] The temperature response information and internal resistance adaptation information can be extracted from the preprocessed battery feedback information through the software's signal separation and data classification algorithms. Specifically, the system first uses a data classification algorithm to preliminarily classify the feedback information by type and group the data sources of different sensors; for example, temperature-related data (such as ambient temperature and battery surface temperature change rate) are classified into the temperature response information group, while the current change rate, battery internal resistance, and voltage data are classified into the internal resistance adaptation information group. Next, the system extracts features from each group, and further identifies the fluctuation characteristics in the temperature response information (such as temperature change trends and hysteresis characteristics), as well as the current and voltage correlation characteristics in the internal resistance adaptation information (such as the adaptability of internal resistance to current changes) through a pattern recognition algorithm. Through this separation and feature extraction, the system can accurately and automatically identify the key information required for temperature response and internal resistance adaptation, so that it can be used to calculate the temperature hysteresis coefficient and internal resistance adaptation index later.

[0069] The extracted temperature response information and internal resistance adaptation information are analyzed to generate the temperature hysteresis coefficient and internal resistance adaptation index respectively.

[0070] In this embodiment, the logic for obtaining the temperature hysteresis coefficient is as follows:

[0071] Extract the temperature response information from the preprocessed battery feedback information at the initial stage of charging of the portable charging pile, specifically including the actual temperature of the battery surface and the actual temperature of the environment at different times during a period of time during the initial stage of charging of the portable charging pile, and represent them respectively with functions TB(t) and TE(t) according to the time series, where t is the time point, and the time period is defined as [t1, t2]. TB(t) represents the actual temperature of the battery surface at time t during a period of time during the initial stage of charging of the portable charging pile, and TE(t) represents the actual temperature of the environment at time t during a period of time during the initial stage of charging of the portable charging pile;

[0072] The temperature response information can be extracted from the sensor feedback of the portable charging pile through the data acquisition module and software processing algorithm, including the battery surface temperature and ambient temperature at different times during the initial period of charging. The system first collects a series of temperature data in the early stage of charging in real time through the ambient temperature sensor and battery surface temperature sensor of the portable charging pile. At fixed sampling periods (such as every second or every millisecond), the sensor transmits the real-time ambient temperature data and battery surface temperature data to the main control system. The data acquisition module in the main control system sorts and stores the data according to the timestamp, and pairs the ambient temperature at each moment with the battery surface temperature to form a temperature change data set over a period of time. Subsequently, the data processing software extracts the temperature response information at the initial stage of charging from this data set to ensure that the system can capture the temperature change pattern over time. These real-time collected temperature data provide the basic basis for the subsequent calculation of the temperature hysteresis coefficient, making the analysis of the entire temperature response more accurate and timely.

[0073] Calculate the temperature hysteresis coefficient. The specific calculation formula is as follows:

[0074]

[0075] Where TLC is the temperature hysteresis coefficient.

[0076] This formula is used to calculate the temperature hysteresis coefficient TLC, which reflects the degree of hysteresis in the battery's response to temperature changes in a low temperature environment. The calculation process of the formula comprehensively evaluates the battery's adaptability to ambient temperature from the perspective of temperature difference and temperature change rate. First, the integral term The temperature difference within the time period [t1, t2] is accumulated, where |TE(t)-TB(t)| represents the absolute difference between the ambient temperature TE(t) and the battery surface temperature TB(t) at each time t, reflecting the lag of the battery temperature relative to the ambient temperature. It is the rate of change of the battery surface temperature, which is used to indicate the response speed of the battery temperature to time. Dividing the temperature difference by the rate of change can standardize the degree of hysteresis at different moments and obtain a relative value. Finally, by integrating and averaging the entire time period, the comprehensive response of the battery to temperature changes during the time period is calculated, so that the temperature hysteresis coefficient TLC can quantify the temperature adaptability of the battery and provide a basis for subsequent current setting.

[0077] The size of the temperature hysteresis coefficient TLC directly reflects the response speed of the battery to temperature changes in a low temperature environment, which in turn affects the battery's matching degree to the initial charging current demand. A larger temperature hysteresis coefficient means that the battery temperature responds more slowly to the ambient temperature, indicating that the battery is less active at low temperatures and requires a higher initial charging current to accelerate the activation process to shorten the charging time and improve efficiency; on the contrary, a smaller temperature hysteresis coefficient indicates that the battery has better adaptability to the ambient temperature, and the temperature changes rapidly and tends to be stable, indicating that the current initial charging current can meet the battery needs and no major adjustments are required. Therefore, by evaluating the size of the temperature hysteresis coefficient, it is possible to accurately determine whether the battery requires a higher or lower initial charging current at low temperatures, thereby optimizing the charging activation speed and efficiency.

[0078] In this embodiment, the logic for obtaining the internal resistance adaptation index is as follows:

[0079] Extract the internal resistance adaptation information from the pre-processed battery feedback information of the portable charging pile at the initial stage of charging, including the actual battery voltage, actual internal resistance and initial current value at different times during the initial period of charging of the portable charging pile, and calibrate them as VB m , RB m and IO m ,VB m Indicates the actual voltage of the battery at time m during the initial period of charging at the portable charging station, RB m It represents the actual internal resistance of the battery at time m during the initial period of charging at the portable charging station, IO m represents the initial current value at time m during the initial period of charging of the portable charging pile, m = 1, 2, 3, ..., g, g is a positive integer;

[0080] The internal resistance adaptation information can be extracted from the battery feedback information at the initial charging stage of the portable charging pile through the data acquisition module and the software processing algorithm, including the actual battery voltage, actual battery internal resistance and initial current value at different times during a period of time in the initial charging stage. The system first collects these feedback data in real time through the sensors in the portable charging pile. Specifically, the actual battery voltage is continuously collected by the voltage sensor within a fixed sampling period, and the actual battery internal resistance is measured and transmitted in real time by the internal resistance measurement module at each sampling time. The initial current value is obtained by the current monitoring module of the portable charging pile. During the acquisition process, the main control system timestamps these data to ensure that each set of data corresponds to the precise sampling time. Subsequently, the software processing algorithm organizes the voltage, internal resistance and current values ​​at each moment to form a data sequence over a period of time, thereby forming a complete internal resistance adaptation information for calculating the internal resistance adaptation index. This real-time acquisition and sorting method ensures the accuracy and timeliness of the data, providing a reliable data basis for subsequent analysis.

[0081] Calculate the internal resistance adaptation index. The specific calculation formula is as follows:

[0082]

[0083] Where IRAI is the internal resistance adaptation index.

[0084] This formula is used to calculate the internal resistance adaptation index IRAI, which aims to evaluate the adaptability of the battery to the initial current setting at the beginning of charging. The formula first performs a weighted summation of the actual battery voltage, battery internal resistance and initial current value at different times during a period of time at the beginning of charging to reflect the adaptability of the battery internal resistance to the current initial current. Specifically, Molecular IO m+1 -IO m The increment of the initial current at adjacent moments is calculated to represent the change of the current over time; the denominator VB m -IO m *RB m It is the actual voltage minus the voltage drop caused by the internal resistance, which is used to quantify the correlation between the current change and the internal resistance. By summing and averaging these ratios, the formula obtains the overall internal resistance adaptation index. The higher the IRAI value, the better the match between the current internal resistance of the battery and the initial current setting. This calculation method can accurately evaluate the battery's adaptability to current in a low temperature environment and help the system determine whether the initial current setting needs to be adjusted to optimize charging efficiency and safety.

[0085] The size of the internal resistance adaptation index IRAI directly reflects the degree of match between the battery's internal resistance and the initial charging current, thereby helping to evaluate the battery's matching degree of the initial charging current requirements in a low temperature environment. A higher internal resistance adaptation index means that the battery's internal resistance and the currently set initial current have better adaptability, indicating that the battery can effectively withstand the current setting and achieve a higher charging efficiency at low temperatures without significantly adjusting the current; on the contrary, a lower internal resistance adaptation index indicates that the internal resistance and the initial current setting do not match, and the battery's ability to accept current is poor, which may lead to low charging efficiency or slow activation speed. At this time, the system needs to increase the initial charging current to enhance the charging activation speed. Therefore, the size of IRAI provides a basis for the system to dynamically adjust the initial current, making the charging process in a low temperature environment more efficient and safe.

[0086] An initial current setting evaluation model is constructed for the generated temperature hysteresis coefficient and internal resistance adaptation index to generate an initial current evaluation coefficient, and an analysis is performed after the generation to evaluate the matching degree of the battery's initial charging current requirements in a low temperature environment, and the evaluation results are divided into three situations: complete charging current matching, insufficient charging current matching, and excessive charging current matching;

[0087] In this embodiment, an initial current setting evaluation model is constructed based on the generated temperature hysteresis coefficient and internal resistance adaptation index to generate an initial current evaluation coefficient, specifically including the following steps:

[0088] Collect several temperature hysteresis coefficients, internal resistance adaptation indexes and corresponding initial current evaluation coefficients generated in the past period of time, and calibrate them as TLC x IRAI x and ICEC x , x represents the number of temperature hysteresis coefficients, internal resistance adaptation indexes and corresponding initial current evaluation coefficients generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set;

[0089] The temperature hysteresis coefficient, internal resistance adaptation index and corresponding initial current evaluation coefficient generated in the past period of time can be automatically collected through the data management module and the database system. Specifically, after the system calculates the temperature hysteresis coefficient, internal resistance adaptation index and initial current evaluation coefficient each time, it stores these data entries together with the generated timestamp in the database. The data management module regularly queries these records and filters them according to a preset time range (such as the past month or a specific charging time) to extract historical data that meets the time conditions. In order to ensure the integrity and traceability of the data, the system assigns a unique identifier to each data entry and arranges them in chronological order to form an ordered historical data set. In this way, the system can automatically and efficiently collect and organize evaluation data over a period of time, providing accurate data support for subsequent model training and regression analysis.

[0090] Limiting x to a positive integer greater than or equal to 3 is to ensure that at least three independent equations can be formed in the multiple regression model through sufficient data points so as to solve the three regression coefficients simultaneously. Only when there are at least three different historical data entries (that is, at least three different combinations of temperature hysteresis coefficients, internal resistance adaptation indexes, and initial current evaluation coefficients) can a system of equations be formed, and the optimal solution for each regression coefficient can be obtained by simultaneous solution. In addition, more data points (x greater than 3) not only meet the minimum calculation requirements, but also improve the fitting accuracy of the regression model, making the solved regression coefficients more statistically robust and reliable, thereby enhancing the prediction accuracy of the model and reducing the impact of errors on the results. Therefore, setting x≥3 is to ensure the feasibility of the mathematical solution and the accuracy of the model.

[0091] Select the multivariate regression model as the initial current setting evaluation model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0092] ICEC x =β0+β1*TLC x -β2*IRAI x

[0093] Where β0, β1 and β2 are regression coefficients;

[0094] The multiple regression model is a statistical method used to analyze the linear relationship between multiple independent variables and dependent variables and predict the value of the dependent variable by fitting the best linear equation. The multiple regression model is selected as the initial current setting evaluation model because we need to include the temperature hysteresis coefficient TLC x and internal resistance adaptation index IRAI x As an independent variable, the initial current evaluation coefficient ICEC x As the dependent variable, to establish the relationship between the two and ICECx Through training with historical data sets, the system can determine the values ​​of regression coefficients β0, β1, and β2 according to the changes in actual data, so that the model can accurately predict ICEC. x The value of is used to evaluate the battery's matching degree to the initial charging current requirement. Where β0 is a constant term, which represents the benchmark ICEC when both the temperature hysteresis coefficient and the internal resistance adaptation index are zero. x Value; β1 represents the temperature hysteresis coefficient to ICEC x The influence weight of β reflects the impact of battery temperature response on the initial current demand; β2 represents the impact of internal resistance adaptation index on ICEC x The influence weight of the battery internal resistance adaptability reflects the impact of the initial current demand. By determining these regression coefficients, the model can adjust the initial current setting more accurately and optimize the matching and efficiency of the charging process.

[0095] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined;

[0096] The purpose of optimizing the regression coefficients by minimizing the error between the predicted values ​​and the actual values ​​is to ensure that the multivariate regression model can reflect the impact of the temperature hysteresis coefficient and the internal resistance adaptation index on the initial current evaluation coefficient as accurately as possible, thereby improving the model's prediction accuracy for the matching degree of battery charging demand. Specifically, the optimization process usually uses the least squares method, that is, calculating the sum of squared errors between the model predicted values ​​and the actual observed values, optimizing the sum of squared errors as the objective function, and gradually adjusting the values ​​of the regression coefficients β0, β1, and β2 until the error is minimized. Through this process, the model can find the coefficient combination that best suits the historical data, so that the weight of the impact of the temperature hysteresis coefficient and the internal resistance adaptation index on the initial current demand is accurately expressed, thereby providing more reliable initial current setting recommendations in future applications.

[0097] The final determined regression coefficient is used to set the constructed initial current evaluation model, and the real-time generated temperature hysteresis coefficient TLC and internal resistance adaptation index IRAI are input to generate the initial current evaluation coefficient ICEC in real time.

[0098] In this embodiment, the generated initial current evaluation coefficient ICEC is compared with the preset initial current evaluation coefficient threshold interval [ICEC min , ICEC max ] to compare, and evaluate the matching degree of the battery's initial charging current requirements in a low temperature environment according to the comparison results, and divide it into three situations according to the evaluation results: complete charging current matching, insufficient charging current matching, and excessive charging current matching. The specific comparison analysis and division are as follows:

[0099] If ICEC <ICEC min , the current initial charging current is insufficient to match the battery's demand in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is insufficiently matched with the charging current;

[0100] This situation means that the current initial charging current is insufficient to meet the battery activation requirements in a low temperature environment. Since the battery is less active in a low temperature environment, a low charging current will cause the battery to enter the charging state for a longer time, thus affecting the charging efficiency. This undermatching situation will increase the user's waiting time, especially in low temperature conditions, the battery may not be activated quickly, resulting in a delay in the charging process, which will affect the normal use of electric vehicles and travel arrangements.

[0101] If ICEC min ≤ICEC≤ICEC max , the current initial charging current fully matches the battery's demand for the initial charging current in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is fully matched to the charging current;

[0102] This situation indicates that the current initial charging current fully matches the battery's requirements in a low temperature environment. This state indicates that the initial current set by the system can effectively activate the battery without overcharging, and the battery can smoothly enter the charging state at low temperatures. Charging efficiency and safety are optimally balanced in this matching state. The system does not need to adjust the current and can maintain stable charging, thereby improving the user's charging experience and ensuring that the battery life and safety are not affected.

[0103] If ICEC>ICEC max , the current initial charging current overmatches the battery's demand in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is overmatched.

[0104] This situation means that the current charging current is excessive and exceeds the actual demand of the battery in a low temperature environment. Excessive current may cause the internal heat of the battery to increase, thereby accelerating battery aging and even bringing safety risks such as battery overheating or short circuit. In addition, in a low temperature environment, excessive charging current may cause an imbalance in internal chemical reactions and affect the long-term health of the battery. This situation increases the risk of battery loss and may cause safety problems, so it is necessary to reduce the charging current in time to avoid damage to the battery.

[0105] The "pre-set initial current evaluation coefficient threshold interval" can be determined through statistical analysis based on historical data and machine learning model training. First, the system collects a large amount of historical data, including the temperature hysteresis coefficient, internal resistance adaptation index and its corresponding initial current evaluation coefficient value under different low temperature environments, and pre-processes these data to ensure the accuracy and consistency of the data. Then, use data analysis software to perform distribution statistics on these data to identify the relationship between different ICEC values ​​and the degree of matching between charging requirements. Through cluster analysis or regression analysis, the software can find the ICEC range corresponding to different charging matching states (such as insufficient matching, complete matching and excessive matching). Finally, use a machine learning model (such as a classification model or a regression model) to further train these data to determine the most appropriate ICEC min and ICEC max The threshold value enables the interval to accurately distinguish different matching states. This process ensures the universality and accuracy of the threshold interval in various low-temperature environments, thereby providing a reliable standard for the dynamic adjustment of the charging current.

[0106] According to the division results of the matching degree of the initial charging current demand, corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures are taken respectively;

[0107] In this embodiment, according to the division result of the matching degree of the initial charging current demand, corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures are respectively adopted, specifically:

[0108] For the case where the classification result is insufficient charging current matching, the specific measures taken are: increasing the set value of the initial charging current to increase the activation speed of the battery in a low temperature environment so that it can achieve the required charging efficiency;

[0109] In this case, the software can increase the initial charging current setting through an increase adjustment algorithm. The specific implementation method is: the system first calculates the required current increase, and obtains the optimal increase value through a comprehensive analysis of historical data and current environmental data. Then, the software control module transmits the newly set increase value to the charging control system to achieve automatic adjustment of the charging current. Increasing the initial current setting helps to quickly activate the battery at low temperatures and shorten the time to enter the charging state, thereby improving charging efficiency and avoiding charging delays caused by insufficient current.

[0110] When the division result is that the charging current is completely matched, the specific measures taken are: maintain the current set value of the initial charging current without further adjustment, and continuously monitor the battery status to ensure the stability of the matching degree;

[0111] The current initial charging current fully meets the battery requirements, and the software will perform maintenance measures without adjusting the charging current. The specific implementation method is: the software system first confirms that the current current matching is within the optimal range, and then maintains the current current setting unchanged by continuously monitoring the charging status. In order to ensure the continuous stability of the charging state, the system will periodically collect temperature, internal resistance and other data, update ICEC in real time, and verify the matching of the charging current. In this way, unnecessary current fluctuations can be avoided while maintaining stable charging, thereby optimizing the charging experience and ensuring the safety and life of the battery.

[0112] For the case where the classification result is excessive charging current matching, the specific measures taken are: reducing the set value of the initial charging current to prevent battery overheating and overcharging, thereby ensuring the safety of the charging process and the service life of the battery.

[0113] The current charging current is too high, which may cause the risk of overheating or overcharging the battery. At this time, the software reduces the initial charging current through the reduction optimization algorithm. The specific implementation method is: the system calculates the optimal reduction ratio according to the degree to which ICEC exceeds the threshold, and transmits the value to the charging control module to automatically reduce the charging current. The purpose of this is to prevent battery loss and safety risks caused by excessive current, thereby ensuring the safety of the charging process in a low temperature environment, extending the battery life, and preventing the battery from being damaged by excessive current.

[0114] Continuously monitor battery status information during the charging process and dynamically update the initial current setting evaluation model based on real-time analysis results to ensure the safety and efficiency of the charging process under low temperature conditions.

[0115] Continuous monitoring of battery status information during the charging process can be achieved through the real-time monitoring module embedded in the system. First, the system will access multiple sensors, including temperature sensors, internal resistance detection modules and voltage sensors, to collect key data such as the battery's ambient temperature, battery temperature, internal resistance, charging voltage and current in real time. At fixed sampling intervals, the monitoring module uploads this data to the main control system and timestamps it to ensure the time consistency of the data. Then, the data management module performs pre-processing such as cleaning and denoising on this data to ensure the accuracy of the data and provide high-quality input information for subsequent dynamic analysis.

[0116] After the monitored battery status information is uploaded, the system will use real-time data to dynamically update the initial current setting evaluation model. Specifically, the analysis software in the main control system will compare the currently collected data with the historical data of the model, calculate the new temperature hysteresis coefficient and internal resistance adaptation index, and recalculate the initial current evaluation coefficient ICEC through these parameters. If it is found that the current ICEC exceeds the pre-set threshold range, the system will automatically adjust the regression coefficient in the regression model so that the model can more accurately reflect the matching degree of the battery's charging requirements under the current low temperature conditions. This process realizes real-time optimization of the model through machine learning algorithms (such as online regression), enabling the system to respond quickly to changes in the environment and battery status.

[0117] The purpose of continuously monitoring and dynamically updating the initial current setting evaluation model is to ensure the safety and efficiency of the charging process under low temperature conditions. In low temperature environments, the temperature and internal resistance state of the battery may fluctuate rapidly. If not adjusted in real time, the charging current setting may deviate from the optimal value, thereby affecting charging efficiency or increasing the risk of battery damage. By updating the model in real time, the system can quickly respond to changes in battery status and ensure that the current setting always matches the battery requirements, thereby optimizing charging speed, reducing the risk of battery loss, and improving the safety of the overall charging process and user experience.

[0118] like Figure 2 The electric vehicle charging management system based on low temperature compensation shown includes an initial demand analysis module, a feedback analysis generation module, an evaluation model building module, an intelligent current regulation module, and a dynamic monitoring update module;

[0119] The initial demand analysis module obtains the initial low-temperature demand information of the battery when the electric vehicle is charged by a portable charging pile in the low-temperature environment in the early morning of winter, and establishes an initial current setting analysis framework for the battery demand under low-temperature conditions based on the obtained initial low-temperature demand information of the battery;

[0120] The feedback analysis generation module obtains the battery feedback information of the portable charging pile in real time at the initial stage of charging, and analyzes it after acquisition to generate the temperature hysteresis coefficient and internal resistance adaptation index respectively;

[0121] The evaluation model building module builds an initial current setting evaluation model for the generated temperature hysteresis coefficient and internal resistance adaptation index, generates an initial current evaluation coefficient, and analyzes it after generation to evaluate the battery's demand matching degree for the initial charging current in a low temperature environment, and divides it into three situations according to the evaluation results: complete charging current matching, insufficient charging current matching, and excessive charging current matching;

[0122] The intelligent current regulation module takes corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures according to the division results of the matching degree of the initial charging current demand;

[0123] The dynamic monitoring update module continuously monitors the battery status information during the charging process and dynamically updates the initial current setting evaluation model based on the real-time analysis results to ensure the safety and efficiency of the charging process under low temperature conditions.

[0124] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0125] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0126] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0127] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0128] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0129] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0131] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An electric vehicle charging management method based on low temperature compensation, characterized in that: The specific steps include: In a low temperature environment in the early morning of winter, when an electric vehicle is charged through a portable charging pile, the initial low temperature demand information of the battery is obtained, and an initial current setting analysis framework for the battery demand under low temperature conditions is established based on the obtained initial low temperature demand information of the battery; Real-time acquisition of battery feedback information at the initial stage of charging of a portable charging pile, and analysis after acquisition to generate a temperature hysteresis coefficient and an internal resistance adaptation index; An initial current setting evaluation model is constructed for the generated temperature hysteresis coefficient and internal resistance adaptation index to generate an initial current evaluation coefficient, and an analysis is performed after the generation to evaluate the matching degree of the battery's initial charging current requirements in a low temperature environment, and the evaluation results are divided into three situations: complete charging current matching, insufficient charging current matching, and excessive charging current matching; According to the division results of the matching degree of the initial charging current demand, corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures are taken respectively; Continuously monitor battery status information during the charging process and dynamically update the initial current setting evaluation model based on real-time analysis results to ensure the safety and efficiency of the charging process under low temperature conditions.

2. The electric vehicle charging management method based on low temperature compensation according to claim 1 is characterized in that: The battery feedback information of the portable charging pile at the initial stage of charging is obtained in real time, and analyzed after acquisition to generate a temperature hysteresis coefficient and an internal resistance adaptation index, respectively, which specifically includes the following steps: Real-time acquisition of battery feedback information at the initial stage of charging of portable charging piles, and pre-processing after acquisition; Extracting temperature response information and internal resistance adaptation information from pre-processed battery feedback information of the portable charging pile at the initial stage of charging; The extracted temperature response information and internal resistance adaptation information are analyzed to generate the temperature hysteresis coefficient and internal resistance adaptation index respectively.

3. The electric vehicle charging management method based on low temperature compensation according to claim 2 is characterized in that: The acquisition logic of the temperature hysteresis coefficient is as follows: Extract the temperature response information from the preprocessed battery feedback information at the initial stage of charging of the portable charging pile, specifically including the actual temperature of the battery surface and the actual temperature of the environment at different times during a period of time during the initial stage of charging of the portable charging pile, and represent them respectively with functions TB(t) and TE(t) according to the time series, where t is the time point, and the time period is defined as [t1, t2]. TB(t) represents the actual temperature of the battery surface at time t during a period of time during the initial stage of charging of the portable charging pile, and TE(t) represents the actual temperature of the environment at time t during a period of time during the initial stage of charging of the portable charging pile; Calculate the temperature hysteresis coefficient. The specific calculation formula is as follows: Where TLC is the temperature hysteresis coefficient.

4. The electric vehicle charging management method based on low temperature compensation according to claim 3 is characterized in that: The logic for obtaining the internal resistance adaptation index is as follows: Extract the internal resistance adaptation information from the pre-processed battery feedback information of the portable charging pile at the initial stage of charging, including the actual battery voltage, actual internal resistance and initial current value at different times during the initial period of charging of the portable charging pile, and calibrate them as VB m , RB m and IO m ,VB m Indicates the actual voltage of the battery at time m during the initial period of charging at the portable charging station, RB m It represents the actual internal resistance of the battery at time m during the initial period of charging at the portable charging station, IO m represents the initial current value at time m during the initial period of charging of the portable charging pile, m = 1, 2, 3, ..., g, g is a positive integer; Calculate the internal resistance adaptation index. The specific calculation formula is as follows: Where IRAI is the internal resistance adaptation index.

5. The electric vehicle charging management method based on low temperature compensation according to claim 4 is characterized in that: An initial current setting evaluation model is constructed for the generated temperature hysteresis coefficient and internal resistance adaptation index to generate an initial current evaluation coefficient, specifically including the following steps: Collect several temperature hysteresis coefficients, internal resistance adaptation indexes and corresponding initial current evaluation coefficients generated in the past period of time, and calibrate them as TLC x IRAI x and ICEC x , x represents the number of temperature hysteresis coefficients, internal resistance adaptation indexes and corresponding initial current evaluation coefficients generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set; Select the multivariate regression model as the initial current setting evaluation model, and train it through the historical data set to determine the value of the regression coefficient according to the formula: ICEC x =β0+β1*TLC x -β2*IRAI x Where β0, β1 and β2 are regression coefficients; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined; The final determined regression coefficient is used to set the constructed initial current evaluation model, and the real-time generated temperature hysteresis coefficient TLC and internal resistance adaptation index IRAI are input to generate the initial current evaluation coefficient ICEC in real time.

6. The electric vehicle charging management method based on low temperature compensation according to claim 5 is characterized in that: The generated initial current evaluation coefficient ICEC is compared with the preset initial current evaluation coefficient threshold interval [ICEC min , ICEC max ] to compare, and evaluate the matching degree of the battery's initial charging current requirements in a low temperature environment according to the comparison results, and divide it into three situations according to the evaluation results: complete charging current matching, insufficient charging current matching, and excessive charging current matching. The specific comparison analysis and division are as follows: If ICEC <ICEC min , the current initial charging current is insufficient to match the battery's demand in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is insufficiently matched with the charging current; If ICEC min ≤ICEC≤ICEC max , the current initial charging current fully matches the battery's demand for the initial charging current in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is fully matched to the charging current; If ICEC>ICEC max , the current initial charging current overmatches the battery's demand in a low temperature environment, and the battery's demand for the initial charging current in a low temperature environment is overmatched.

7. The electric vehicle charging management method based on low temperature compensation according to claim 6 is characterized in that: According to the division results of the matching degree of the initial charging current demand, corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures are taken respectively, specifically: For the case where the classification result is insufficient charging current matching, the specific measures taken are: increasing the set value of the initial charging current to increase the activation speed of the battery in a low temperature environment so that it can achieve the required charging efficiency; When the division result is that the charging current is completely matched, the specific measures taken are: maintain the current set value of the initial charging current without further adjustment, and continuously monitor the battery status to ensure the stability of the matching degree; For the case where the classification result is excessive charging current matching, the specific measures taken are: reducing the set value of the initial charging current to prevent battery overheating and overcharging, thereby ensuring the safety of the charging process and the service life of the battery.

8. An electric vehicle charging management system based on low temperature compensation, used to implement the electric vehicle charging management method based on low temperature compensation as described in any one of claims 1 to 7, characterized in that: It includes an initial demand analysis module, a feedback analysis generation module, an evaluation model building module, an intelligent current regulation module, and a dynamic monitoring update module; The initial demand analysis module obtains the initial low-temperature demand information of the battery when the electric vehicle is charged by a portable charging pile in the low-temperature environment in the early morning of winter, and establishes an initial current setting analysis framework for the battery demand under low-temperature conditions based on the obtained initial low-temperature demand information of the battery; The feedback analysis generation module obtains the battery feedback information of the portable charging pile in real time at the initial stage of charging, and analyzes it after acquisition to generate the temperature hysteresis coefficient and internal resistance adaptation index respectively; The evaluation model building module builds an initial current setting evaluation model for the generated temperature hysteresis coefficient and internal resistance adaptation index, generates an initial current evaluation coefficient, and analyzes it after generation to evaluate the battery's demand matching degree for the initial charging current in a low temperature environment, and divides it into three situations according to the evaluation results: complete charging current matching, insufficient charging current matching, and excessive charging current matching; The intelligent current regulation module takes corresponding initial current increase adjustment, initial current reduction optimization and charging current maintenance measures according to the division results of the matching degree of the initial charging current demand; The dynamic monitoring update module continuously monitors the battery status information during the charging process and dynamically updates the initial current setting evaluation model based on the real-time analysis results to ensure the safety and efficiency of the charging process under low temperature conditions.

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