Method and system for increasing charging speed of electric vehicle

Abstract

The invention relates to the technical field of battery charging control, and discloses a method and system for improving the charging speed of an electric vehicle, and the method comprises the steps: counting the number of single cells in an electric vehicle battery pack to construct an equivalent electrical structure of the electric vehicle battery pack, and calculating a current bearing threshold value of the electric vehicle battery pack; generating an aging state curve of the electric vehicle battery pack, and calculating a charging tolerance coefficient of the electric vehicle battery pack; analyzing an effective temperature signal in the temperature monitoring signal to calculate a temperature rise constraint threshold value of the electric vehicle battery pack; and generating a charging optimization condition of the electric vehicle battery pack, when the charging optimization condition meets a preset accelerated charging condition, identifying an accelerated charging interval in the electric vehicle battery pack, and executing quick charging operation of the electric vehicle battery pack to obtain a charging result. The charging speed of the electric vehicle can be increased.

Description

Technical Field

[0001] This invention relates to a method and system for improving the charging speed of electric vehicles, belonging to the field of battery charging control technology. Background Technology

[0002] With the rapid development of the electric vehicle industry, charging speed has become a key factor restricting its further popularization. Electric vehicles are favored by consumers for their advantages such as energy saving and environmental protection. However, their charging time far exceeds the refueling time of traditional fuel vehicles, which greatly reduces the user experience.

[0003] Currently, most electric vehicles rely on traditional charging technology, which regulates the charging process solely based on the battery's basic charge parameters. This lacks comprehensive consideration of key parameters across multiple dimensions, such as battery aging, real-time temperature changes, and electrical characteristics. This judgment and control method cannot accurately reflect the actual state of the battery. For example, as the battery ages, its internal resistance increases, but this method cannot adjust the charging strategy in a timely manner according to this change, resulting in low charging efficiency. In low-temperature environments, the battery's chemical reaction rate slows down, and this method fails to fully consider the impact of temperature on battery performance, thus extending the charging time of electric vehicles. Summary of the Invention

[0004] This invention provides a method and system for improving the charging speed of electric vehicles, the main purpose of which is to improve the charging speed of electric vehicles.

[0005] To achieve the above objectives, the present invention provides a method for improving the charging speed of electric vehicles, comprising:

[0006] Obtain the electric vehicle battery pack to be charged, count the number of individual cells in the electric vehicle battery pack to construct the equivalent electrical structure of the electric vehicle battery pack, and calculate the current withstand threshold of the electric vehicle battery pack based on the equivalent electrical structure.

[0007] The system schedules historical charging data corresponding to the electric vehicle battery pack, generates an aging state curve for the electric vehicle battery pack based on the historical charging data, and calculates the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve.

[0008] The temperature monitoring signal of the electric vehicle battery pack is collected, and the effective temperature signal in the temperature monitoring signal is analyzed to calculate the temperature rise constraint threshold of the electric vehicle battery pack.

[0009] By combining the current withstand threshold, the charging tolerance coefficient, and the temperature rise constraint threshold, charging optimization conditions for the electric vehicle battery pack are generated. When the charging optimization conditions meet the preset accelerated charging conditions, the accelerated charging interval in the electric vehicle battery pack is identified. Based on the accelerated charging interval, the fast charging operation of the electric vehicle battery pack is executed to obtain the charging result.

[0010] Optionally, establishing the equivalent electrical structure of the electric vehicle battery pack based on the number of cells includes:

[0011] Obtain the electrical connection relationship between the individual battery cells;

[0012] Based on the electrical connection relationship, determine the connection method between the individual battery cells;

[0013] Based on the connection method, the electrical behavior of the electric vehicle battery pack during the power-on process is analyzed;

[0014] By combining the number of battery cells with the electrical behavior, the equivalent electrical structure of the electric vehicle battery pack is established.

[0015] Optionally, estimating the current withstand threshold corresponding to the electric vehicle battery pack based on the equivalent electrical structure includes:

[0016] Determine the total resistance and allowable voltage corresponding to the equivalent electrical structure;

[0017] The equivalent electrical structure is subjected to an energization test to obtain an electrical test circuit;

[0018] Measure the actual terminal voltage across the electrical test circuit and record the test current value in the test circuit;

[0019] Combining the total resistance, the allowable voltage, the actual terminal voltage, and the test current value, the current withstand threshold corresponding to the electric vehicle battery pack is estimated using the following method:

[0020]

[0021] Where A represents the current withstand threshold of the electric vehicle battery pack. Indicates the test current value. Indicates the allowable voltage. Indicates the actual terminal voltage. This represents the total resistance.

[0022] Optionally, generating the aging state curve of the electric vehicle battery pack based on the historical charging data includes:

[0023] Extract the charging capacity sequence and charging cycle count from the historical charging data;

[0024] Anomalies are removed from the charging capacity sequence and the number of charging cycles respectively to obtain the effective capacity sequence and the effective number of cycles.

[0025] The effective capacity sequence and the effective number of cycles are normalized respectively to obtain the standard capacity value and the standard cycle value.

[0026] Based on the standard capacity value and the standard cycle value, an aging distribution scatter plot of the electric vehicle battery pack is constructed;

[0027] Regression analysis was performed on the scatter plot of the aging distribution to obtain the regression aging curve;

[0028] The regression aging curve is smoothed to obtain the aging state curve corresponding to the electric vehicle battery pack.

[0029] Optionally, calculating the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve includes:

[0030] Read the characteristic decay points on the aging state curve, and calculate the average decay rate of the aging state curve based on the characteristic decay points.

[0031] Based on the average degradation rate, the capacity degradation rate of the electric vehicle battery pack is determined, and the performance retention rate corresponding to the capacity degradation rate is calculated.

[0032] Identify the inflection point position on the aging state curve, and determine the starting point of accelerated degradation of the electric vehicle battery pack based on the inflection point position;

[0033] Count the number of charging cycles corresponding to the acceleration degradation start point, and query the total number of charging cycles of the electric vehicle battery pack;

[0034] Combining the number of charging cycles, the total number of charging cycles, and the performance retention rate, the charging tolerance coefficient of the electric vehicle battery pack is calculated using the following formula:

[0035]

[0036] Where B represents the charging tolerance coefficient of the electric vehicle battery pack. Indicates performance retention rate. Indicates the number of charging cycles. This indicates the total number of charging cycles.

[0037] Optionally, parsing the effective temperature signal in the temperature monitoring signal includes:

[0038] The temperature monitoring signal is subjected to baseline calibration processing to obtain a calibrated temperature signal;

[0039] The calibration temperature signal is subjected to interference suppression processing to obtain an interference-suppressed signal;

[0040] Identify the effective signal segments in the interference suppression signal;

[0041] Extract the signal amplitude corresponding to the effective signal segment from the suppressed interference signal;

[0042] The effective temperature signal is reconstructed based on the signal amplitude.

[0043] Optionally, calculating the temperature rise constraint threshold of the electric vehicle battery pack based on the effective temperature signal includes:

[0044] The effective temperature signal is quantized and encoded to obtain a temperature characterization value;

[0045] Obtain the ambient reference temperature of the current environment in which the electric vehicle battery pack is located;

[0046] The difference between the temperature characterization value and the ambient reference temperature is calculated to obtain the instantaneous temperature rise value;

[0047] Query the thermal safety threshold temperature of the battery cell chemistry type of the electric vehicle battery pack;

[0048] The theoretical limit temperature rise is obtained by calculating the difference between the thermal safety threshold temperature and the ambient reference temperature.

[0049] Based on the instantaneous temperature rise value and the theoretical limit temperature rise value, the temperature rise constraint threshold of the electric vehicle battery pack is calculated.

[0050] Optionally, the quantization and encoding process of the effective temperature signal to obtain the temperature characterization value includes:

[0051] Analyze the signal waveform corresponding to the effective temperature signal and extract the waveform feature parameters corresponding to the signal waveform;

[0052] Based on the waveform feature parameters, the signal quantization interval corresponding to the effective temperature signal is determined;

[0053] Query the segmented mapping relationship corresponding to the signal quantization interval, and calculate the interval calibration factor corresponding to the signal quantization interval;

[0054] Based on the segmented mapping relationship and the interval calibration factor, the temperature characterization value corresponding to the effective temperature signal is calculated.

[0055] Optionally, calculating the temperature rise constraint threshold of the electric vehicle battery pack based on the instantaneous temperature rise value and the theoretical limit temperature rise value includes:

[0056] Identify the safety margin factor corresponding to the theoretical limit temperature rise value;

[0057] Based on the safety margin coefficient, the theoretical limit temperature rise value is attenuated to obtain the allowable temperature rise value.

[0058] Obtain the real-time temperature rise rate of the electric vehicle battery pack and query the maximum allowable temperature rise rate corresponding to the electric vehicle battery pack.

[0059] Calculate the ratio between the real-time temperature rise rate and the maximum allowable temperature rise rate to obtain the rate over-limit ratio;

[0060] The risk adjustment factor is obtained by calculating the ratio of the real-time temperature rise rate to the maximum allowable temperature rise rate.

[0061] Combining the risk adjustment factor, the rate over-limit ratio, and the allowable temperature rise value, the temperature rise constraint threshold of the electric vehicle battery pack is calculated using the following formula:

[0062]

[0063] Where H represents the temperature rise constraint threshold of the electric vehicle battery pack. Indicates the allowable temperature rise value. Indicates the rate over-limit ratio. This represents the risk adjustment factor.

[0064] To address the above problems, the present invention also provides a system for improving the charging speed of electric vehicles, the system comprising:

[0065] The current withstand threshold calculation module is used to obtain the electric vehicle battery pack to be charged, count the number of individual cells in the electric vehicle battery pack to construct the equivalent electrical structure of the electric vehicle battery pack, and calculate the current withstand threshold of the electric vehicle battery pack based on the equivalent electrical structure.

[0066] The charging tolerance coefficient calculation module is used to schedule the historical charging data corresponding to the electric vehicle battery pack, generate the aging state curve of the electric vehicle battery pack based on the historical charging data, and calculate the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve.

[0067] The temperature rise constraint threshold calculation module is used to collect the temperature monitoring signal of the electric vehicle battery pack, parse the effective temperature signal in the temperature monitoring signal, and calculate the temperature rise constraint threshold of the electric vehicle battery pack.

[0068] The fast charging processing module is used to combine the current withstand threshold, the charging tolerance coefficient and the temperature rise constraint threshold to generate charging optimization conditions for the electric vehicle battery pack. When the charging optimization conditions meet the preset accelerated charging conditions, the module identifies the accelerated charging interval in the electric vehicle battery pack, and performs fast charging operation on the electric vehicle battery pack based on the accelerated charging interval to obtain the charging result.

[0069] Compared to the problems described in the background art, this invention establishes an equivalent electrical structure of the electric vehicle battery pack based on the number of battery cells, reflecting the electrical composition relationship of the electric vehicle battery pack. This provides support for subsequently estimating the current withstand threshold of the electric vehicle battery pack. Furthermore, by generating an aging state curve of the electric vehicle battery pack based on historical charging data, this invention can trace the state evolution process of the electric vehicle battery pack during charge-discharge cycles, thus providing a data foundation for the subsequent calculation of the charging tolerance coefficient of the electric vehicle battery pack. Further, by collecting temperature monitoring signals of the electric vehicle battery pack, this invention can obtain the original temperature information of the electric vehicle battery pack during operation. By analyzing the effective temperature signals in the temperature monitoring signals, the effective temperature components can be separated, thus providing a data foundation for the subsequent calculation of the temperature rise constraint threshold. Finally, this invention generates charging optimization conditions by combining the current withstand threshold, the charging tolerance coefficient, and the temperature rise constraint threshold. When these conditions meet preset accelerated charging conditions, the accelerated charging range of the electric vehicle battery pack is identified, thereby achieving fast charging processing of the electric vehicle battery pack. Therefore, the method and system for improving the charging speed of electric vehicles provided by the embodiments of this invention can improve the charging speed of electric vehicles. Attached Figure Description

[0070] Figure 1 This is a flowchart illustrating a method for improving the charging speed of an electric vehicle according to an embodiment of the present invention.

[0071] Figure 2 This is a schematic diagram of a reproducibility detection process in a method for improving the charging speed of an electric vehicle, provided in an embodiment of the present invention.

[0072] Figure 3 This is a schematic diagram of a module for implementing a system for improving the charging speed of an electric vehicle, provided as an embodiment of the present invention.

[0073] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0074] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0075] This application provides a method for improving the charging speed of electric vehicles. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for improving the charging speed of electric vehicles can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0076] Reference Figure 1 The diagram shown is a flowchart illustrating a method for improving the charging speed of an electric vehicle according to an embodiment of the present invention. In this embodiment, the method for improving the charging speed of an electric vehicle includes:

[0077] S1. Obtain the electric vehicle battery pack to be charged, count the number of individual cells in the electric vehicle battery pack to construct the equivalent electrical structure of the electric vehicle battery pack, and calculate the current carrying capacity threshold of the electric vehicle battery pack based on the equivalent electrical structure.

[0078] This invention establishes an equivalent electrical structure for the electric vehicle battery pack based on the number of battery cells, reflecting the electrical composition of the battery pack and thus providing support for subsequent estimation of the current carrying capacity threshold of the battery pack. The electric vehicle battery pack is a battery system requiring charging and monitoring; the individual battery cell is the basic unit constituting the battery pack; the number of battery cells refers to the total number of individual battery cells; and the equivalent electrical structure is an abstract representation of the electrical connection relationships of the battery pack. Optionally, the individual battery cells in the battery pack can be obtained through a battery management system; the number of battery cells can be calculated by the battery management system.

[0079] As an embodiment of the present invention, establishing the equivalent electrical structure of the electric vehicle battery pack based on the number of battery cells includes:

[0080] Obtain the electrical connection relationship between the individual battery cells;

[0081] Based on the electrical connection relationship, determine the connection method between the individual battery cells;

[0082] Based on the connection method, the electrical behavior of the electric vehicle battery pack during the power-on process is analyzed;

[0083] By combining the number of battery cells with the electrical behavior, the equivalent electrical structure of the electric vehicle battery pack is established.

[0084] The electrical connection relationship refers to the wire connection between the individual battery cells, and the connection method includes series or parallel connection. The electrical behavior represents the changes in current and voltage when energized. Optionally, the electrical connection relationship can be extracted from the battery specification. The connection method can be obtained by inspecting the internal wiring of the battery pack. The inference of the electrical behavior can be based on Ohm's law and basic circuit principles. The establishment of the equivalent electrical structure can be achieved using simplified circuit representation methods.

[0085] This invention calculates the current withstand threshold corresponding to the electric vehicle battery pack based on the equivalent electrical structure, thereby understanding the maximum current that the electric vehicle battery pack can safely pass through during charging, which helps to reasonably control the charging process. The current withstand threshold is used to indicate the upper limit of the current that the electric vehicle battery pack can tolerate during charging, and is used to evaluate the charging adaptability of the battery pack.

[0086] As an embodiment of the present invention, estimating the current withstand threshold corresponding to the electric vehicle battery pack based on the equivalent electrical structure includes:

[0087] Determine the total resistance and allowable voltage corresponding to the equivalent electrical structure;

[0088] The equivalent electrical structure is subjected to an energization test to obtain an electrical test circuit;

[0089] Measure the actual terminal voltage across the electrical test circuit and record the test current value in the test circuit;

[0090] By combining the total resistance, the allowable voltage, the actual terminal voltage, and the test current value, the current withstand threshold corresponding to the electric vehicle battery pack is estimated using the following method.

[0091] Wherein, the total resistance represents the overall resistance of the equivalent electrical structure to the current, the allowable voltage is the highest voltage that the electric vehicle battery pack can withstand during charging, the test circuit is the loop formed by the equivalent electrical structure in the energized state, the actual terminal voltage is the voltage exhibited by the test circuit when actually energized, and the test current value is the magnitude of the current applied during the test.

[0092] Furthermore, the overall resistance can be calculated using a resistance synthesis method based on the series and parallel relationships between the cells; the allowable voltage can be obtained from the battery pack's technical manual; the test circuit can be constructed using an external power supply and load; the terminal voltage can be read by connecting a voltage measuring device; and the test current value can be detected by a current measuring device.

[0093] Furthermore, as another embodiment of the present invention, the current withstand threshold corresponding to the electric vehicle battery pack is estimated by combining the total resistance, the allowable voltage, the actual terminal voltage, and the test current value using the following method:

[0094]

[0095] Where A represents the current withstand threshold of the electric vehicle battery pack. Indicates the test current value. Indicates the allowable voltage. Indicates the actual terminal voltage. This represents the total resistance.

[0096] It should be noted that the current carrying capacity calculation formula focuses on the voltage-current-resistance coupling characteristics of the battery pack's equivalent electrical structure. It constructs a mathematical model by analyzing the terminal voltage deviation (the difference between the allowable voltage and the actual terminal voltage) under test conditions, combined with the constraint effect of the overall resistance on the current carrying capacity. Its core assumption is that the equivalent overall resistance of the battery pack is transiently stable during the test period, and that the voltage-current relationship satisfies an extension of Ohm's law (considering the linear superposition of internal resistance voltage drops): by applying the test current... Perform terminal voltage deviation normalization ( (relative proportion representing voltage margin), then... Correcting the nonlinear distortion under high current (the denominator term reflects the consumption of voltage margin by the resistance-current product) enables the quantitative derivation of the maximum current that the battery can withstand.

[0097] In specific applications, such as DC fast charging scenarios for electric vehicles: real-time acquisition of the actual terminal voltage monitored by the Battery Management System (BMS). Test current The overall resistance was identified online using the AC impedance method. Substitute the values ​​into the formula to dynamically calculate the current withstand threshold A. When the battery experiences cycling aging... When the resistance is increased from the initial 50mΩ to 80mΩ, the traditional fixed current strategy is prone to triggering protection due to overvoltage. However, this formula can adjust the allowable current in real time according to the change in resistance. Conversely, if the battery is in good health, the allowable current can be increased compared to the rated value.

[0098] S2. Schedule the historical charging data corresponding to the electric vehicle battery pack, generate the aging state curve of the electric vehicle battery pack based on the historical charging data, and calculate the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve.

[0099] This invention generates an aging state curve for the electric vehicle battery pack based on the historical charging data, enabling the tracking of the battery pack's state evolution during charge-discharge cycles. This provides a data foundation for calculating the battery pack's charging tolerance coefficient. The historical charging data consists of charging-related information recorded during previous charging processes. The performance change information reflects the battery pack's state changes as usage time or cycle count increases. The aging state curve is a graphical description of the performance change information, used to visually demonstrate the degree of aging. Optionally, the historical charging data corresponding to the electric vehicle battery pack can be obtained through a storage unit in the battery management system. Battery packs are typically equipped with data recording functions, capable of storing information such as voltage, current, temperature, and capacity for each charge.

[0100] As an embodiment of the present invention, generating the aging state curve of the electric vehicle battery pack based on the historical charging data includes:

[0101] Extract the charging capacity sequence and charging cycle count from the historical charging data;

[0102] Anomalies are removed from the charging capacity sequence and the number of charging cycles respectively to obtain the effective capacity sequence and the effective number of cycles.

[0103] The effective capacity sequence and the effective number of cycles are normalized respectively to obtain the standard capacity value and the standard cycle value.

[0104] Based on the standard capacity value and the standard cycle value, an aging distribution scatter plot of the electric vehicle battery pack is constructed;

[0105] Regression analysis was performed on the scatter plot of the aging distribution to obtain the regression aging curve;

[0106] The regression aging curve is smoothed to obtain the aging state curve corresponding to the electric vehicle battery pack.

[0107] Wherein, the charging capacity sequence and the number of charging cycles are data sets of battery capacity and charging cycles recorded in the historical charging data; the effective capacity sequence and the effective number of cycles are capacity and cycle count data after removing abnormal records, respectively; the standard capacity value and the standard cycle value are values ​​after dimension unification processing; the aging distribution scatter plot is a distribution diagram of the relationship between the standard capacity value and the standard cycle value; and the regression aging curve is a trend line reflecting the relationship between capacity decay and the number of cycles.

[0108] Optionally, the extraction of charging capacity sequence and charging cycle count from the historical charging data can be achieved through data query statements, such as SELECT cycle_num, charge_cap FROM charge_log; outlier removal of the charging capacity sequence and charging cycle count can be achieved through percentile filtering; normalization of the effective capacity sequence and effective cycle count can be achieved through minimum-maximum scaling; the construction of the aging distribution scatter plot can be achieved through a data visualization library, such as using the plt.scatter() function to draw the scatter distribution; regression analysis of the aging distribution scatter plot can be achieved using multinomial regression; and smoothing of the regression aging curve can be achieved through moving average.

[0109] This invention calculates the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve. The charging tolerance coefficient can be used to understand the adaptability of the electric vehicle battery pack to fast charging in the current state, providing a basis for determining the subsequent accelerated charging range. The charging tolerance coefficient is used to measure the magnitude of the charging stress that the electric vehicle battery pack can withstand in a specific aging stage.

[0110] As an embodiment of the present invention, calculating the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve includes:

[0111] Read the characteristic decay points on the aging state curve, and calculate the average decay rate of the aging state curve based on the characteristic decay points.

[0112] Based on the average degradation rate, the capacity degradation rate of the electric vehicle battery pack is determined, and the performance retention rate corresponding to the capacity degradation rate is calculated.

[0113] Identify the inflection point position on the aging state curve, and determine the starting point of accelerated degradation of the electric vehicle battery pack based on the inflection point position;

[0114] Count the number of charging cycles corresponding to the acceleration degradation start point, and query the total number of charging cycles of the electric vehicle battery pack;

[0115] The charging tolerance coefficient of the electric vehicle battery pack is calculated using the following formula, which combines the number of charging cycles, the total number of charging cycles, and the performance retention rate.

[0116] Among them, the characteristic decay point is a representative capacity decay node on the aging curve, used to fit the overall decay trend; the average decay rate reflects the average speed at which the battery capacity decreases with the number of cycles; the capacity degradation rate reflects the cumulative decay degree; the performance retention rate intuitively reflects the proportion of the battery's current performance relative to its initial state; the inflection point is the critical point at which the battery transitions from slow decay to rapid decay; the accelerated decay starting point marks the battery aging entering the later stage, at which point the charging intensity needs to be strictly limited; and the total number of charging cycles is the number of cycles in the design life.

[0117] Furthermore, the selection of the characteristic decay point can be achieved through equal-interval sampling (e.g., taking a capacity value every 50 cycles); the calculation of the average decay rate adopts the method of linear fitting of the characteristic decay point; the identification of the inflection point position can be achieved through second derivative mutation detection (when the absolute value of the second derivative exceeds a preset value, it is determined to be an inflection point); the total number of charging cycles can call the cycle life index in the battery's factory parameters (e.g., lithium iron phosphate batteries are usually set to 3000 cycles).

[0118] Furthermore, as another embodiment of the present invention, the calculation of the charging tolerance coefficient of the electric vehicle battery pack using the following formula, combining the number of charging cycles, the total number of charging cycles, and the performance retention rate, includes:

[0119]

[0120] Where B represents the charging tolerance coefficient of the electric vehicle battery pack. Indicates performance retention rate. Indicates the number of charging cycles. This indicates the total number of charging cycles.

[0121] S3. Collect the temperature monitoring signal of the electric vehicle battery pack, analyze the effective temperature signal in the temperature monitoring signal, and calculate the temperature rise constraint threshold of the electric vehicle battery pack.

[0122] This invention acquires the raw temperature information of the electric vehicle battery pack during operation by collecting temperature monitoring signals. Furthermore, by analyzing the effective temperature signal within the temperature monitoring signal, the effective temperature component can be separated, providing a data basis for subsequent calculation of the temperature rise constraint threshold. The temperature monitoring signal is the raw electrical signal output by a temperature sensing device deployed on the electric vehicle battery pack, and the effective temperature signal is a pure temperature characterization signal after removing environmental interference and measurement noise. Moreover, the acquisition of the temperature monitoring signal corresponding to the electric vehicle battery pack can be achieved using temperature sensors such as thermocouples or thermistors.

[0123] As an embodiment of the present invention, the step of parsing the effective temperature signal in the temperature monitoring signal includes:

[0124] The temperature monitoring signal is subjected to baseline calibration processing to obtain a calibrated temperature signal;

[0125] The calibration temperature signal is subjected to interference suppression processing to obtain an interference-suppressed signal;

[0126] Identify the effective signal segments in the interference suppression signal;

[0127] Extract the signal amplitude corresponding to the effective signal segment from the suppressed interference signal;

[0128] The effective temperature signal is reconstructed based on the signal amplitude.

[0129] The calibration temperature signal is a signal after eliminating the zero drift and initial error of the sensor itself; the interference suppression signal is a signal after weakening external interference such as electromagnetic coupling; the effective signal segment is the main part of the signal waveform that clearly reflects the change in battery temperature; and the signal amplitude is the key amplitude value in the effective signal segment that characterizes the temperature level.

[0130] Furthermore, the baseline calibration processing of the temperature monitoring signal can be achieved through a hardware zeroing circuit or software digital filtering; the interference suppression processing of the calibration temperature signal can be achieved through shielding technology or differential amplification method; the identification of the effective signal segment in the suppressed interference signal can be achieved through the amplitude threshold method; and the extraction of the signal amplitude corresponding to the effective signal segment can be achieved through a peak hold circuit or a sample hold algorithm.

[0131] This invention calculates the temperature rise constraint threshold of the electric vehicle battery pack based on the effective temperature signal, thereby determining the highest allowable temperature safety limit of the electric vehicle battery pack during charging or discharging, providing a key basis for thermal management control. The temperature rise constraint threshold represents the maximum allowable temperature rise of the electric vehicle battery pack relative to the ambient temperature while ensuring battery safety and lifespan.

[0132] As an embodiment of the present invention, the step of calculating the temperature rise constraint threshold of the electric vehicle battery pack based on the effective temperature signal includes:

[0133] The effective temperature signal is quantized and encoded to obtain a temperature characterization value;

[0134] Obtain the ambient reference temperature of the current environment in which the electric vehicle battery pack is located;

[0135] The difference between the temperature characterization value and the ambient reference temperature is calculated to obtain the instantaneous temperature rise value;

[0136] Query the thermal safety threshold temperature of the battery cell chemistry type of the electric vehicle battery pack;

[0137] The theoretical limit temperature rise is obtained by calculating the difference between the thermal safety threshold temperature and the ambient reference temperature.

[0138] Based on the instantaneous temperature rise value and the theoretical limit temperature rise value, the temperature rise constraint threshold of the electric vehicle battery pack is calculated.

[0139] The temperature characterization value is a quantified value formed by digital conversion of the effective temperature signal, which can directly reflect the real-time temperature level of the core area of ​​the battery pack; the ambient reference temperature is the base temperature of the external environment where the battery pack is located (such as the temperature inside the vehicle or the outside air temperature); the instantaneous temperature rise value is the increase in the current temperature of the battery pack relative to the ambient temperature, reflecting the temperature deviation caused by the heat generated by the battery itself; the thermal safety threshold temperature is the highest safe temperature that the cell chemical system can withstand (such as 60°C for ternary lithium batteries); the theoretical limit temperature rise is the allowable temperature rise space between the ambient temperature and the thermal safety threshold temperature; and the temperature rise constraint threshold is the maximum temperature rise that the battery pack can safely withstand starting from the instantaneous temperature rise value under the current state.

[0140] Furthermore, the quantization and encoding of the effective temperature signal can be achieved through a 16-bit high-precision analog-to-digital converter, ensuring that the resolution of the temperature characterization value reaches 0.02℃; the acquisition of the ambient reference temperature can be achieved through temperature and humidity sensors deployed outside the battery pack, with the sampling frequency consistent with the effective temperature signal (e.g., 1 time / second); the calculation of the instantaneous temperature rise value adopts the absolute difference method (temperature characterization value - ambient reference temperature), and the result is taken as positive; the query of the thermal safety threshold temperature can be achieved by calling the chemical type parameter table built into the battery pack (the parameter table pre-stores safe temperature data for different cell systems); the calculation of the theoretical limit temperature rise adopts the difference method (thermal safety threshold temperature - ambient reference temperature), and if the result is negative, it is forcibly set to 0 (indicating that the current ambient temperature has exceeded the safe range).

[0141] Furthermore, as an optional embodiment of the present invention, the quantization and encoding processing of the effective temperature signal to obtain the temperature characterization value includes:

[0142] Analyze the signal waveform corresponding to the effective temperature signal and extract the waveform feature parameters corresponding to the signal waveform;

[0143] Based on the waveform feature parameters, the signal quantization interval corresponding to the effective temperature signal is determined;

[0144] Query the segmented mapping relationship corresponding to the signal quantization interval, and calculate the interval calibration factor corresponding to the signal quantization interval;

[0145] Based on the segmented mapping relationship and the interval calibration factor, the temperature characterization value corresponding to the effective temperature signal is calculated.

[0146] The signal waveform refers to the morphological characteristics of the effective temperature signal over time (e.g., waveform shapes of stable, rising, and fluctuating segments); the waveform characteristic parameters are quantization indicators describing the signal waveform, including temperature fluctuation amplitude, heating rate, steady-state duration, and waveform curvature (used to distinguish between linear and nonlinear heating); the signal quantization interval is a temperature value range divided according to the waveform characteristic parameters (e.g., fluctuation amplitude < 1℃ is the stable interval, heating rate > 0.5℃ / min is the rapid heating interval); the segmented mapping relationship is the correspondence rule between the temperature analog value and the digital code within each quantization interval (e.g., 8-bit code is used for the stable interval, and 16-bit code is used for the rapid heating interval); the interval calibration factor is a dynamic coefficient used to correct interval mapping deviations (calculated based on historical coding errors and current environmental interference intensity); and the temperature characterization value is a digital sequence that accurately reflects the actual temperature of the battery pack after quantization, coding, and calibration.

[0147] Furthermore, the waveform analysis and feature parameter extraction of the effective temperature signal can be achieved through time-domain analysis and the sliding window method (the window length is set to 2 seconds, and the extreme values ​​and slopes within the window are extracted); the determination of the signal quantization interval can be achieved through the threshold comparison method (preset the critical thresholds of fluctuation amplitude and heating rate, and match the corresponding interval range); the query of the segmented mapping relationship can be achieved through a preset interval-encoding lookup table (the table stores the starting code and step size of each interval); the calculation of the interval calibration factor can be achieved through the least squares method (fitting the deviation curve between the historical measured temperature value and the encoded inversion value to obtain the real-time correction coefficient); firstly, the initial value of the effective temperature signal in the corresponding interval is obtained according to the segmented mapping relationship, and then the initial value is corrected by interval using the interval calibration factor, so as to obtain the temperature characterization value corresponding to the effective temperature signal.

[0148] Furthermore, as an optional embodiment of the present invention, calculating the temperature rise constraint threshold of the electric vehicle battery pack based on the instantaneous temperature rise value and the theoretical limit temperature rise value includes:

[0149] Identify the safety margin factor corresponding to the theoretical limit temperature rise value;

[0150] Based on the safety margin coefficient, the theoretical limit temperature rise value is attenuated to obtain the allowable temperature rise value.

[0151] Obtain the real-time temperature rise rate of the electric vehicle battery pack and query the maximum allowable temperature rise rate corresponding to the electric vehicle battery pack.

[0152] Calculate the ratio between the real-time temperature rise rate and the maximum allowable temperature rise rate to obtain the rate over-limit ratio;

[0153] The risk adjustment factor is obtained by calculating the ratio of the real-time temperature rise rate to the maximum allowable temperature rise rate.

[0154] The temperature rise constraint threshold of the electric vehicle battery pack is calculated using the following formula, which combines the risk adjustment factor, the rate over-limit ratio, and the allowable temperature rise value.

[0155] The safety margin coefficient is a buffer coefficient set to prevent the theoretical limit temperature rise value from reaching the actual safety critical point, reflecting the degree of safety redundancy; the allowable temperature rise value is the actual tolerable upper limit of temperature rise after the theoretical limit temperature rise value is attenuated by the safety margin coefficient; the real-time temperature rise rate is the temperature rise rate of the battery pack per unit time during the current charging process; the maximum allowable temperature rise rate is the upper limit of the temperature rise rate that the battery pack has verified in long-term use and will not cause performance degradation; the rate over-limit ratio is used to measure the degree to which the real-time temperature rise rate approaches the maximum allowable temperature rise rate, and the smaller the ratio, the further away from the over-limit state; the risk adjustment factor is a risk quantification index generated based on the relationship between the real-time temperature rise rate and the maximum allowable temperature rise rate, and the larger the ratio, the higher the current temperature rise risk.

[0156] Furthermore, the safety margin coefficient corresponding to the theoretical limit temperature rise value can be identified by referring to the provisions on safety redundancy in the battery pack design manual; the allowable temperature rise value can be calculated by multiplying the theoretical limit temperature rise value by the reciprocal of the safety margin coefficient; the real-time temperature rise rate of the electric vehicle battery pack can be obtained by continuously collecting temperature data by a temperature sensor and calculating the temperature difference per unit time; the maximum allowable temperature rise rate can be obtained from the battery pack's instruction manual; the calculation of the rate over-limit ratio and the risk adjustment factor are both completed by directly comparing the real-time temperature rise rate with the maximum allowable temperature rise rate; when the rate over-limit ratio is greater than 1, it indicates that the real-time temperature rise rate has exceeded the maximum allowable temperature rise rate. At this time, the weight of the allowable temperature rise value needs to be reduced by the numerator in the formula, and the temperature rise constraint threshold is further compressed by the denominator due to the increase of the risk adjustment factor, so as to strengthen the safety constraint.

[0157] Furthermore, as another embodiment of the present invention, the temperature rise constraint threshold of the electric vehicle battery pack is calculated using the following formula, combining the risk adjustment factor, the rate over-limit ratio, and the allowable temperature rise value:

[0158]

[0159] Where H represents the temperature rise constraint threshold of the electric vehicle battery pack. Indicates the allowable temperature rise value. Indicates the rate over-limit ratio. This represents the risk adjustment factor.

[0160] It should be noted that the temperature rise constraint threshold calculation formula decomposes the temperature rise control requirements of electric vehicle battery packs into safety redundancy adjustments for allowable temperature rise and inverse constraints on risk factors. By reducing the impact of rate over-limit ratio on the allowable temperature rise value and then amplifying the safety margin with a risk adjustment factor, a temperature control model is constructed. Its core is based on battery thermal characteristics, assuming that the rate over-limit ratio and the risk adjustment factor act independently on the temperature rise constraint. It uses a combination of linear correction and fractional scaling to quantify the dynamic contraction of the safety boundary with temperature rise risk, achieving precise limitation on battery pack temperature rise.

[0161] In practical applications, such as in fast charging scenarios for electric vehicles, this formula can transform the complex temperature rise risk of the battery into a calculable constraint threshold. For example, in situations where there is simultaneous high-current fast charging (trigger rate over-limit ratio) and high-temperature summer environment (improving risk adjustment factor), the temperature rise constraint threshold is accurately compressed, reducing the probability of the battery's actual temperature rise exceeding the threshold. By adjusting the safety margin coefficient to adapt to different battery chemistry systems (such as ternary lithium and lithium iron phosphate), the accuracy of temperature rise constraint is improved for scenarios with fluctuating charging current. It effectively identifies early signals of abnormal battery pack temperature rise trends (such as sudden changes in temperature rise rate within 10 seconds), providing a trigger basis for the battery thermal management system to intervene and adjust in advance, ensuring the safety of the fast charging process.

[0162] S4. Combining the current withstand threshold, the charging tolerance coefficient, and the temperature rise constraint threshold, generate the charging optimization conditions for the electric vehicle battery pack. When the charging optimization conditions meet the preset accelerated charging conditions, identify the accelerated charging interval in the electric vehicle battery pack. Based on the accelerated charging interval, perform the fast charging operation of the electric vehicle battery pack to obtain the charging result.

[0163] This invention generates charging optimization conditions by combining the current withstand threshold, the charging tolerance coefficient, and the temperature rise constraint threshold. When these conditions meet preset accelerated charging conditions, the accelerated charging range of the electric vehicle battery pack is identified, thereby achieving fast charging of the electric vehicle battery pack. The charging optimization conditions are control criteria formed by combining the constraints of these three factors on the charging process. The preset accelerated charging conditions are the simultaneous fulfillment of three conditions: a current withstand threshold sufficient to support higher currents, a stable charging tolerance coefficient within a tolerable range, and a temperature rise constraint threshold not reaching the trigger protection threshold. The accelerated charging range is the energy range within which the battery pack, in its current state, can maintain a safe temperature and voltage even after increasing the charging power (e.g., within the 20%–80% energy range, battery internal resistance, polarization effects, and other characteristics support faster charging).

[0164] Furthermore, the process for identifying the accelerated charging zone is as follows: continuously monitor the voltage change rate and temperature rise during normal battery charging; when the voltage change is gradual and the temperature rise rate is below the warning value, mark the corresponding power range for that period as the accelerated charging zone; when performing fast charging, gradually increase the charging power to a preset acceleration level, while simultaneously tracking the battery pack temperature and voltage in real time; if a sudden temperature rise or abnormal voltage fluctuation occurs, immediately adjust the charging power to ensure the charging process remains within safe boundaries, ultimately obtaining charging results including charging time and power increase. Specifically, for a more intuitive understanding of the lighting processing flow of a method for improving electric vehicle charging speed in this application, please refer to [reference needed]. Figure 2 The diagram shown is a schematic representation of the identification process in a method for improving the charging speed of electric vehicles provided by the present invention. It should be noted that in this invention, Figure 2 The flowchart presented is only for the identification process of one method for improving the charging speed of electric vehicles, and is not limited to the identification and processing of one method for improving the charging speed of electric vehicles in different actual application scenarios.

[0165] Compared to the problems described in the background art, this invention establishes an equivalent electrical structure of the electric vehicle battery pack based on the number of battery cells, reflecting the electrical composition relationship of the electric vehicle battery pack. This provides support for subsequently estimating the current withstand threshold of the electric vehicle battery pack. Furthermore, by generating an aging state curve of the electric vehicle battery pack based on historical charging data, this invention can trace the state evolution process of the electric vehicle battery pack during charge-discharge cycles, thus providing a data foundation for the subsequent calculation of the charging tolerance coefficient of the electric vehicle battery pack. Further, by collecting temperature monitoring signals of the electric vehicle battery pack, this invention can obtain the original temperature information of the electric vehicle battery pack during operation. By analyzing the effective temperature signals in the temperature monitoring signals, the effective temperature components can be separated, thus providing a data foundation for the subsequent calculation of the temperature rise constraint threshold. Finally, this invention generates charging optimization conditions by combining the current withstand threshold, the charging tolerance coefficient, and the temperature rise constraint threshold. When these conditions meet preset accelerated charging conditions, the accelerated charging range of the electric vehicle battery pack is identified, thereby achieving fast charging processing of the electric vehicle battery pack. Therefore, the method and system for improving the charging speed of electric vehicles provided by the embodiments of this invention can improve the charging speed of electric vehicles.

[0166] like Figure 3 The diagram shown is a functional block diagram of a system for improving the charging speed of electric vehicles according to the present invention.

[0167] The system 200 for improving the charging speed of electric vehicles described in this invention can be installed in an electronic device. Depending on the functions implemented, the system may include a sample screening module 201, a sample coating analysis module 202, a coating defect analysis module 203, and a coating spraying optimization module 204. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0168] In this embodiment of the invention, the functions of each module / unit are as follows:

[0169] The current withstand threshold calculation module 201 is used to obtain the electric vehicle battery pack to be charged, count the number of individual cells in the electric vehicle battery pack to construct the equivalent electrical structure of the electric vehicle battery pack, and calculate the current withstand threshold of the electric vehicle battery pack based on the equivalent electrical structure.

[0170] The charging tolerance coefficient calculation module 202 is used to schedule the historical charging data corresponding to the electric vehicle battery pack, generate the aging state curve of the electric vehicle battery pack based on the historical charging data, and calculate the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve.

[0171] The temperature rise constraint threshold calculation module 203 is used to collect the temperature monitoring signal of the electric vehicle battery pack, parse the effective temperature signal in the temperature monitoring signal, and calculate the temperature rise constraint threshold of the electric vehicle battery pack.

[0172] The fast charging processing module 204 is used to combine the current withstand threshold, the charging tolerance coefficient and the temperature rise constraint threshold to generate charging optimization conditions for the electric vehicle battery pack. When the charging optimization conditions meet the preset accelerated charging conditions, it identifies the accelerated charging interval in the electric vehicle battery pack, and performs fast charging operation on the electric vehicle battery pack based on the accelerated charging interval to obtain the charging result.

[0173] In detail, the modules in the system 200 for improving the charging speed of electric vehicles described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method described herein is the same as the one used to improve the charging speed of electric vehicles, and can produce the same technical effect, so it will not be repeated here.

[0174] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0175] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for improving the charging speed of electric vehicles, characterized in that, The method includes: Obtain the electric vehicle battery pack to be charged, count the number of individual cells in the electric vehicle battery pack to construct the equivalent electrical structure of the electric vehicle battery pack, and calculate the current withstand threshold of the electric vehicle battery pack based on the equivalent electrical structure. The system schedules historical charging data corresponding to the electric vehicle battery pack, generates an aging state curve for the electric vehicle battery pack based on the historical charging data, and calculates the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve. The temperature monitoring signal of the electric vehicle battery pack is collected, and the effective temperature signal in the temperature monitoring signal is analyzed to calculate the temperature rise constraint threshold of the electric vehicle battery pack. By combining the current withstand threshold, the charging tolerance coefficient, and the temperature rise constraint threshold, charging optimization conditions for the electric vehicle battery pack are generated. When the charging optimization conditions meet the preset accelerated charging conditions, the accelerated charging interval in the electric vehicle battery pack is identified. Based on the accelerated charging interval, the fast charging operation of the electric vehicle battery pack is executed to obtain the charging result.

2. The method for improving the charging speed of electric vehicles as described in claim 1, characterized in that, The step of establishing the equivalent electrical structure of the electric vehicle battery pack based on the number of battery cells includes: Obtain the electrical connection relationship between the individual battery cells; Based on the electrical connection relationship, determine the connection method between the individual battery cells; Based on the connection method, the electrical behavior of the electric vehicle battery pack during the power-on process is analyzed; Based on the number of battery cells and the electrical behavior, the equivalent electrical structure of the electric vehicle battery pack is established.

3. The method for improving the charging speed of electric vehicles as described in claim 1, characterized in that, The step of estimating the current withstand threshold corresponding to the electric vehicle battery pack based on the equivalent electrical structure includes: Determine the total resistance and allowable voltage corresponding to the equivalent electrical structure; The equivalent electrical structure is subjected to an energization test to obtain an electrical test circuit; Measure the actual terminal voltage across the electrical test circuit and record the test current value in the test circuit; Combining the total resistance, the allowable voltage, the actual terminal voltage, and the test current value, the current withstand threshold corresponding to the electric vehicle battery pack is estimated using the following method: Where A represents the current withstand threshold of the electric vehicle battery pack. Indicates the test current value. Indicates the allowable voltage. Indicates the actual terminal voltage. This represents the total resistance.

4. The method for improving the charging speed of electric vehicles as described in claim 1, characterized in that, The step of generating the aging state curve of the electric vehicle battery pack based on the historical charging data includes: Extract the charging capacity sequence and charging cycle count from the historical charging data; Anomalies are removed from the charging capacity sequence and the number of charging cycles respectively to obtain the effective capacity sequence and the effective number of cycles. The effective capacity sequence and the effective number of cycles are normalized respectively to obtain the standard capacity value and the standard cycle value. Based on the standard capacity value and the standard cycle value, an aging distribution scatter plot of the electric vehicle battery pack is constructed; Regression analysis was performed on the scatter plot of the aging distribution to obtain the regression aging curve; The regression aging curve is smoothed to obtain the aging state curve corresponding to the electric vehicle battery pack.

5. A method for improving the charging speed of an electric vehicle as described in claim 1, characterized in that, The calculation of the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve includes: Read the characteristic decay points on the aging state curve, and calculate the average decay rate of the aging state curve based on the characteristic decay points. Based on the average degradation rate, the capacity degradation rate of the electric vehicle battery pack is determined, and the performance retention rate corresponding to the capacity degradation rate is calculated. Identify the inflection point position on the aging state curve, and determine the starting point of accelerated degradation of the electric vehicle battery pack based on the inflection point position; Count the number of charging cycles corresponding to the acceleration degradation start point, and query the total number of charging cycles of the electric vehicle battery pack; Combining the number of charging cycles, the total number of charging cycles, and the performance retention rate, the charging tolerance coefficient of the electric vehicle battery pack is calculated using the following formula: Where B represents the charging tolerance coefficient of the electric vehicle battery pack. Indicates performance retention rate. Indicates the number of charging cycles. This indicates the total number of charging cycles.

6. The method for improving the charging speed of an electric vehicle as described in claim 1, characterized in that, The process of parsing the effective temperature signal in the temperature monitoring signal includes: The temperature monitoring signal is subjected to baseline calibration processing to obtain a calibrated temperature signal; The calibration temperature signal is subjected to interference suppression processing to obtain an interference-suppressed signal; Identify the effective signal segments in the interference suppression signal; Extract the signal amplitude corresponding to the effective signal segment from the suppressed interference signal; The effective temperature signal is reconstructed based on the signal amplitude.

7. A method for improving the charging speed of an electric vehicle as described in claim 1, characterized in that, The calculation of the temperature rise constraint threshold for the electric vehicle battery pack based on the effective temperature signal includes: The effective temperature signal is quantized and encoded to obtain a temperature characterization value; Obtain the ambient reference temperature of the current environment in which the electric vehicle battery pack is located; The difference between the temperature characterization value and the ambient reference temperature is calculated to obtain the instantaneous temperature rise value; Query the thermal safety threshold temperature of the battery cell chemistry type of the electric vehicle battery pack; The theoretical limit temperature rise is obtained by calculating the difference between the thermal safety threshold temperature and the ambient reference temperature. Based on the instantaneous temperature rise value and the theoretical limit temperature rise value, the temperature rise constraint threshold of the electric vehicle battery pack is calculated.

8. A method for improving the charging speed of an electric vehicle as described in claim 7, characterized in that, The quantization and encoding process of the effective temperature signal to obtain the temperature characterization value includes: Analyze the signal waveform corresponding to the effective temperature signal and extract the waveform feature parameters corresponding to the signal waveform; Based on the waveform feature parameters, the signal quantization interval corresponding to the effective temperature signal is determined; Query the segmented mapping relationship corresponding to the signal quantization interval, and calculate the interval calibration factor corresponding to the signal quantization interval; Based on the segmented mapping relationship and the interval calibration factor, the temperature characterization value corresponding to the effective temperature signal is calculated.

9. A method for improving the charging speed of an electric vehicle as described in claim 7, characterized in that, The calculation of the temperature rise constraint threshold for the electric vehicle battery pack based on the instantaneous temperature rise value and the theoretical limit temperature rise value includes: Identify the safety margin factor corresponding to the theoretical limit temperature rise value; Based on the safety margin coefficient, the theoretical limit temperature rise value is attenuated to obtain the allowable temperature rise value. Obtain the real-time temperature rise rate of the electric vehicle battery pack and query the maximum allowable temperature rise rate corresponding to the electric vehicle battery pack. Calculate the ratio between the real-time temperature rise rate and the maximum allowable temperature rise rate to obtain the rate over-limit ratio; The risk adjustment factor is obtained by calculating the ratio of the real-time temperature rise rate to the maximum allowable temperature rise rate. Combining the risk adjustment factor, the rate over-limit ratio, and the allowable temperature rise value, the temperature rise constraint threshold of the electric vehicle battery pack is calculated using the following formula: Where H represents the temperature rise constraint threshold of the electric vehicle battery pack. Indicates the allowable temperature rise value. Indicates the rate over-limit ratio. This represents the risk adjustment factor.

10. A system for increasing the charging speed of electric vehicles, characterized in that, The system includes: The current withstand threshold calculation module is used to obtain the electric vehicle battery pack to be charged, count the number of individual cells in the electric vehicle battery pack to construct the equivalent electrical structure of the electric vehicle battery pack, and calculate the current withstand threshold of the electric vehicle battery pack based on the equivalent electrical structure. The charging tolerance coefficient calculation module is used to schedule the historical charging data corresponding to the electric vehicle battery pack, generate the aging state curve of the electric vehicle battery pack based on the historical charging data, and calculate the charging tolerance coefficient of the electric vehicle battery pack based on the aging state curve. The temperature rise constraint threshold calculation module is used to collect the temperature monitoring signal of the electric vehicle battery pack, parse the effective temperature signal in the temperature monitoring signal, and calculate the temperature rise constraint threshold of the electric vehicle battery pack. The fast charging processing module is used to combine the current withstand threshold, the charging tolerance coefficient and the temperature rise constraint threshold to generate charging optimization conditions for the electric vehicle battery pack. When the charging optimization conditions meet the preset accelerated charging conditions, the module identifies the accelerated charging interval in the electric vehicle battery pack, and performs fast charging operation on the electric vehicle battery pack based on the accelerated charging interval to obtain the charging result.

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