A charging station charging anomaly automatic reporting method and system

Abstract

The application discloses a kind of charging station charging abnormality automatic reporting method and system, applied to the multi-stage constant current charging scene of two-wheeled electric vehicle battery, involve charging safety technical field, method includes obtaining the real-time operating parameter of battery in multi-stage constant current charging process;Determine the current state of battery according to real-time operating parameter, generate a tolerance corridor as safety benchmark based on the current state of battery;According to the monitoring parameter of real-time operating parameter, judge whether monitoring parameter exceeds the upper and lower boundaries of tolerance corridor;If exceed boundary, at least one abnormal risk mode is associated according to the boundary type that exceeds, and trigger the data packet of the identification of corresponding abnormal risk mode is reported.The application generates tolerance corridor based on the current state of battery, fully considers the parameter characteristic difference of different charging stages, avoids the false alarm, miss report problem caused by traditional fixed threshold, makes abnormal determination and the dynamic process of multi-stage constant current charging deep adaptation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery charging safety, in particular to a charging station charging abnormality automatic reporting method and system. BACKGROUND

[0002] With the rapid popularization of new energy transportation system, two-wheeled electric vehicles have become the core transportation tool for urban short-distance commuting and end distribution due to their flexibility, convenience and low-carbon economy. Due to the large number of two-wheeled electric vehicles in some cities, the demand for public intelligent charging stations has surged. However, public charging stations are mostly distributed in densely populated areas such as communities, business districts and distribution sites, and the charging scene is mainly without active cooling conditions. In addition, two-wheeled electric vehicle batteries have various brands and different aging degrees, which are prone to cause safety accidents such as thermal runaway and short circuit due to voltage fluctuations, temperature abnormalities and current surges during charging.

[0003] In the field of public charging of two-wheeled electric vehicles, the existing technology proposes a multi-stage constant current charging control method with different optimization directions, which adjusts the charging current at each stage to improve charging efficiency and basic safety. For example, the conventional optimized uniform multi-stage constant current charging control strategy and the multi-stage constant current charging optimization strategy with improved PSO algorithm disclosed in CN120828698A. However, in the abnormal monitoring link of multi-stage constant current charging, the existing technology has defects. Traditional abnormal monitoring mostly uses fixed threshold judgment method, which is not adapted to the optimization control model of multi-stage constant current charging, and does not consider the differences in current and voltage characteristics at different stages of multi-stage constant current charging, as well as the dynamic influence of battery state of charge and temperature on the safety range of parameters, which is prone to misreporting or missing reporting by determining normal stage fluctuations as abnormal, resulting in insufficient monitoring reference accuracy. In view of the defects of the existing technology, a technical solution is needed that can adapt to the characteristics of multi-stage constant current charging, accurately identify abnormal risks and report them, in order to fill the gap between existing charging control and safety monitoring.

[0004] Therefore, a charging station charging abnormality automatic reporting method and system are needed. SUMMARY

[0005] In view of the problem in the prior art that abnormal monitoring is not adapted to the optimization control model of multi-stage constant current charging, which is prone to misreporting or missing reporting by determining normal stage fluctuations as abnormal, resulting in insufficient monitoring reference accuracy, the present application provides a charging station charging abnormality automatic reporting method and system, which is applied to the multi-stage constant current charging scene of two-wheeled electric vehicle batteries. By dynamically generating a tolerance corridor based on the current state of the battery, the differences in parameter characteristics at different charging stages are fully considered, avoiding the misreporting and missing reporting problems caused by traditional fixed threshold, and making the abnormality determination deeply adapted to the dynamic process of multi-stage constant current charging. The specific technical solution is as follows:

[0006] In a first aspect, the present application provides a method for automatically reporting charging abnormalities of a charging station, which is applied to a multi-stage constant current charging scenario of a battery of a two-wheeled electric vehicle, and comprises the following steps:

[0007] obtaining real-time operating parameters of the battery in a multi-stage constant current charging process;

[0008] determining a current state of the battery according to the real-time operating parameters, generating a tolerance corridor serving as a safety benchmark based on the current state of the battery, the tolerance corridor defining upper and lower boundaries allowing fluctuations of real-time parameters around an expected parameter trajectory, and the width of the upper and lower boundaries being set according to the type of parameters;

[0009] judging whether a monitoring parameter of the real-time operating parameters exceeds the upper and lower boundaries of the tolerance corridor;

[0010] if the boundary is exceeded, associating at least one abnormal risk mode according to the type of the exceeded boundary, and triggering a data packet reporting an identifier of the abnormal risk mode.

[0011] Preferably, the step of generating a tolerance corridor serving as a safety benchmark based on the current state of the battery comprises the following steps:

[0012] calculating an expected parameter trajectory of each monitoring parameter by using a second-order RC equivalent circuit model of the battery based on an optimal charging current value of a current charging stage;

[0013] determining initial width values of the upper and lower boundaries of each monitoring parameter according to the safety characteristics of the monitoring parameter; the monitoring parameter at least includes an end voltage parameter, a battery temperature parameter and a charging current parameter;

[0014] dynamically adjusting the initial width values based on a real-time state of charge value and a real-time temperature value of the battery to obtain adjusted width values;

[0015] generating final tolerance corridor boundaries as safety benchmarks by taking the expected parameter trajectory as the center and combining the adjusted width values.

[0016] Preferably, the method for automatically reporting charging abnormalities of a charging station further comprises the following steps:

[0017] obtaining an ambient temperature and an ambient humidity of an environment where the charging station is located, and relaxing or tightening the boundaries of the tolerance corridor according to the ambient temperature and the ambient humidity.

[0018] Preferably, the step of determining initial width values of the upper and lower boundaries of each monitoring parameter according to the safety characteristics of the monitoring parameter comprises the following steps:

[0019] for the end voltage parameter, the initial width of the upper boundary is proportionally narrower than that of the lower boundary according to the fact that overvoltage risk is higher than undervoltage risk;

[0020] For the temperature parameter, according to the temperature rise risk increases with the current, the upper boundary initial width is linearly tightened with the charging current;

[0021] For the current parameter, according to the existence of transient fluctuation when the stage is switched, the boundary width is widened by a coefficient at the switching time, and the upper and lower boundaries are symmetrically arranged;

[0022] Preferably, the lower boundary initial width of each monitoring parameter is preset as a fixed value based on the battery specification and the charging strategy.

[0023] Preferably, the lower boundary initial width of each monitoring parameter is determined based on the battery specification and the charging strategy.

[0024] Preferably, the dynamic adjustment of the initial width value based on the real-time state of charge value comprises:

[0025] determining a first adjustment coefficient according to the real-time state of charge value;

[0026] When the real-time state of charge value is lower than a first preset threshold, the first adjustment coefficient is set to a first value A1;

[0027] When the real-time state of charge value is between the first preset threshold and a second preset threshold, the first adjustment coefficient is set to a second value A2;

[0028] When the real-time state of charge value is higher than the second preset threshold, the first adjustment coefficient is set to a third value A3;

[0029] Wherein, A1, A3 are less than A2.

[0030] Preferably, the dynamic adjustment of the initial width value based on the real-time temperature value comprises:

[0031] determining a second adjustment coefficient according to the real-time temperature value;

[0032] When the real-time temperature value is lower than a third preset threshold, the second adjustment coefficient is set to a fourth value B1;

[0033] When the real-time temperature value is between the third preset threshold and a fourth preset threshold, the second adjustment coefficient is set to a fifth value B2;

[0034] When the real-time temperature value is higher than the fourth preset threshold, the second adjustment coefficient is set to a sixth value B3;

[0035] Wherein, B1 is greater than B2, and B3 is less than B2.

[0036] Preferably, a charging station charging abnormality automatic reporting method further comprises:

[0037] multiplying the initial width value by the first adjustment coefficient and the second adjustment coefficient to obtain an adjusted width value.

[0038] Preferably, if the boundary is exceeded, at least one abnormal risk mode is associated according to the type of the exceeded boundary, and a data packet corresponding to the identification of the abnormal risk mode is triggered to be reported.

[0039] An event in which each monitoring parameter exceeds the upper boundary or the lower boundary of the tolerance corridor is monitored and identified, and a boundary breakthrough type is determined.

[0040] Based on a preset mapping relationship between the boundary breakthrough type and the abnormal risk mode, at least one abnormal risk mode is associated for the corresponding event, and a data packet is generated and reported.

[0041] Preferably, the method for automatically reporting charging abnormalities of a charging station further comprises:

[0042] According to the abnormal risk mode, the reporting priority of the data packet is adjusted; wherein the reporting priority is divided according to the risk degree of the abnormal risk mode to the safety of the battery.

[0043] In a second aspect, the application further provides a system for automatically reporting charging abnormalities of a charging station, which is used to implement the method described above, and comprises:

[0044] A data acquisition unit is configured to acquire real-time operation parameters of a battery in a multi-stage constant current charging process.

[0045] A safety boundary generation unit is configured to determine a current state of the battery according to the real-time operation parameters, and generate a tolerance corridor serving as a safety reference based on the current state of the battery, wherein the tolerance corridor defines upper and lower boundaries allowing fluctuations of real-time parameters with an expected parameter trajectory as a center, and the widths of the upper and lower boundaries are set to be different from each other according to the parameter type and the charging stage.

[0046] A judgment unit is configured to judge whether a monitoring parameter of the real-time operation parameters exceeds the upper and lower boundaries of the tolerance corridor.

[0047] An abnormality reporting unit is configured to, if the boundary is exceeded, associate at least one abnormal risk mode according to the type of the exceeded boundary, and trigger a data packet corresponding to the identification of the abnormal risk mode to be reported.

[0048] Compared with the prior art, the application has the following beneficial effects:

[0049] The charging station charging abnormality automatic reporting method of the present application generates a tolerance corridor based on the current state of the battery, fully considers the parameter characteristic differences in different charging stages, deeply adapts the abnormality judgment to the dynamic process of multi-stage constant current charging, and avoids the false alarm and missed alarm problems caused by the traditional fixed threshold. More specifically, by using the optimal current value of the current charging stage, the expected parameter trajectory of each parameter is calculated through a second-order RC equivalent circuit model, and the final tolerance corridor is generated based on this, fully adapting to the parameter dynamic change law of multi-stage constant current charging, avoiding the threshold judgment defects, and reducing the false alarm rate and missed alarm rate of abnormality monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0051] Fig. 1 The flow chart of a charging station charging abnormality automatic reporting method of an embodiment of the present application.

[0052] Fig. 2 The flow chart of a tolerance corridor generation method of an embodiment of the present application.

[0053] Fig. 3 The system principle diagram of a charging station charging abnormality automatic reporting method of an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0055] It should be understood that when used in the present specification, the terms "comprise" and "include" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or sets thereof.

[0056] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0057] It should be further understood that the term "and / or" used in the description of the present application means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0058] The following examples are described with reference to Figs. 1 to 3 .

[0059] Traditional charging anomaly monitoring mostly uses fixed threshold judgment mode, which is not adapted to the optimization control model of multi-stage constant current charging, does not consider the current and voltage characteristic differences in different stages of multi-stage constant current charging, and the dynamic influence of battery state of charge and temperature on the safety range of parameters, and is easy to determine normal stage fluctuations as abnormal, resulting in false positives or false negatives, resulting in insufficient accuracy of monitoring reference. There are also some existing multi-stage constant current charging anomaly monitoring combined with artificial intelligence, but since the charging station for two-wheeled electric vehicles adopts a small and boundary layout, it does not have high-power hardware devices, and it is not applicable to monitor and analyze the multi-section charging of two-wheeled electric vehicles through high-power monitoring. In view of this, the present application refers to Fig. 1 , the embodiment of the present application provides a charging station charging anomaly automatic reporting method, which is applied to the multi-stage constant current charging scene of two-wheeled electric vehicle battery, and includes the following steps:

[0060] Step S1, acquiring real-time running parameters of the battery in the multi-stage constant current charging process;

[0061] In the embodiment, a local controller can be set in the charging station, and the local controller is interacted with the battery management system (BMS) and the charging module. After the charging starts, the charging station controller acquires real-time running parameters from the battery management system (BMS) and the charging module through CAN bus or special communication interface at a set sampling frequency. The real-time running parameters include electrical parameters of battery terminal voltage and charging current, thermal parameters of battery surface temperature, state parameters of battery state of charge (SOC) and health state (SOH), and charging strategy parameters corresponding to the current charging stage number and the preset optimal charging current value of the stage.

[0062] In other embodiments, for the two-wheeled electric vehicle battery accessing the charging pile of the charging station, parameters are collected through corresponding sensors. For example, the real-time voltage between the positive and negative electrodes of the battery is collected through a voltage sensor connected in series in the output loop of the charging pile; the surface temperature of the battery is collected through an NTC (negative temperature coefficient thermistor) attached to the middle of the battery shell; the real-time charging current is collected through a Hall current sensor connected in series in the output loop of the charging pile, which is suitable for the current adjustment range of multi-stage constant-current charging; the real-time SOC value is read through the battery management system (BMS), and if the battery does not have a BMS, it is estimated based on the charging capacity and voltage change trend through the built-in algorithm of the charging pile. At the same time, the collected raw data is filtered to eliminate abnormal peak data caused by electromagnetic interference and retain the true operating parameters.

[0063] At the same time, in order to ensure data quality, the collected raw data is filtered and abnormal values are removed for preprocessing, and the preprocessed real-time parameters are transmitted in real time to the charging station management platform through the 4G / 5G module.

[0064] Step S2, determining the current state of the battery according to the real-time operating parameters, generating a tolerance corridor as a safety reference based on the current state of the battery, the tolerance corridor defining an upper and lower boundary allowing the real-time parameters to fluctuate around the expected parameter trajectory, and the width of the upper and lower boundaries being set according to the parameter type;

[0065] A tolerance corridor of a personalized safety reference line is dynamically generated for each key monitoring parameter. The tolerance corridor not only reflects the theoretical expected trajectory of the parameter, but also embodies the risk difference in different directions through the asymmetric boundary setting. Please refer to Fig. 2 Generating a tolerance corridor as a safety reference based on the current state of the battery includes:

[0066] Step S21, based on the optimal charging current value of the current charging stage, calculating the expected parameter trajectory of each monitoring parameter using the second-order RC equivalent circuit model of the battery;

[0067] The second-order RC model parameter table corresponding to the battery model is loaded. The table is obtained through HPPC (Hybrid Pulse Power Characteristic Test) experiment identification, and stores the model parameters ohmic resistance, electrochemical polarization resistance and capacitance, concentration polarization resistance and capacitance, open circuit voltage at different SOC points.

[0068] Using the terminal voltage and charging current collected at the current time t as initial conditions, and combining model parameters, the extended Kalman filter (EKF) algorithm is used to estimate the current electrochemical polarization voltage and concentration polarization voltage. It is assumed that constant current charging will be performed strictly according to the preset optimal charging current for the remaining time of the current charging phase. Based on this premise, the current value is substituted into the battery's state equation for numerical integration, thereby deriving the theoretical change curves of the terminal voltage and internal polarization voltage from the current time to the end of this phase, i.e., the expected voltage trajectory. Simultaneously, combining the battery's heat generation and dissipation model and inputting the current ambient temperature, the expected temperature trajectory of the battery is calculated. The expected trajectory of the charging current is simplified to a constant optimal current value. The specific calculation derivation is as follows:

[0069] Assuming the current time Current charging current Current terminal voltage Current state of charge Current battery temperature Current ambient temperature Optimal charging current during the current charging phase Rated battery capacity .

[0070] Obtain the current parameter from the pre-stored second-order RC model parameter table using bilinear interpolation. and The following model parameters:

[0071]

[0072]

[0073]

[0074]

[0075] Among them, the function This indicates that the parameter value is obtained through table lookup and interpolation. The internal resistance is ohmic; and These are the electrochemical polarization resistance and capacitance, respectively. and These are the concentration polarization resistor and capacitor, respectively. This is the open-circuit voltage;

[0076] State variable initialization (extended Kalman filter) state vector definition:

[0077]

[0078] in, This is the electrochemical polarization voltage. concentration polarization voltage.

[0079] EKF prediction step:

[0080]

[0081]

[0082] where, is the prior state estimate at step k, is the prior estimate error covariance matrix, is the state transition matrix, is the input matrix, is the system input (charging current), is the process noise covariance matrix;

[0083] EKF update step:

[0084]

[0085]

[0086]

[0087] where, is the Kalman gain matrix, is the observation matrix, is the actual observation value (terminal voltage measurement), is the observation noise covariance matrix, is the posterior state estimate at step k, is the posterior estimate error covariance matrix.

[0088] State transition matrix :

[0089]

[0090] Input matrix :

[0091]

[0092] where, is the Coulombic efficiency, is the sampling period, convert Ah to As (Coulombs).

[0093] Observation matrix :

[0094]

[0095] Observation equation:

[0096]

[0097] where, is the open-circuit voltage corresponding to the current SOC, is the charging current at the kth step, is the ohmic internal resistance, and are the electrochemical polarization voltage and the concentration polarization voltage at the kth step, respectively.

[0098] Through the EKF iteration, the optimal estimation value of the state variable at the current time is obtained:

[0099]

[0100] From the current time to the time when the current charging stage ends , the time interval is . The state equation is:

[0101]

[0102]

[0103]

[0104] where, , , are the derivatives of SOC, electrochemical polarization voltage, and concentration polarization voltage with respect to time, respectively; is the coulombic efficiency; is the optimal charging current.

[0105] For each time step , where , :

[0106] SOC update:

[0107]

[0108] where, and are the SOC values at the kth step and the k+1th step, respectively.

[0109] Polarization voltage update:

[0110]

[0111]

[0112] where, , respectively the electrochemical polarization voltage and the concentration polarization voltage of the kth step; , , , respectively the model parameters corresponding to the kth step.

[0113] Model parameter update:

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] wherein, , , , , , is a function to obtain the model parameters by table lookup and interpolation, is the battery temperature of the kth step.

[0121] Expected terminal voltage calculation:

[0122]

[0123] wherein, is the expected terminal voltage of the k+1th step.

[0124] Expected terminal voltage trajectory output:

[0125]

[0126] wherein, is the sequence of expected terminal voltages from to .

[0127] Thermal model parameters The battery thermal parameters include the battery mass , the battery specific heat capacity , the battery surface area , the convective heat transfer coefficient , the thermal time constant .

[0128] Total heat generation power during charging includes:

[0129] Ohmic heat:

[0130] Polarization heat:

[0131] Reversible heat (electrochemical reaction heat):

[0132]

[0133] Total heat power:

[0134]

[0135] where, is the open-circuit voltage temperature coefficient; , , are the ohmic resistance, electrochemical polarization resistance, and concentration polarization resistance at time t, respectively; is the battery temperature at time t.

[0136] Heat dissipation power calculation:

[0137]

[0138] where, is the ambient temperature at time t.

[0139] Temperature dynamic equation continuous-time equation:

[0140]

[0141] where, is the derivative of the battery temperature with respect to time.

[0142] Discrete-time equation (Euler method):

[0143]

[0144] where, and are the battery temperatures at the kth step and the k+1th step, respectively.

[0145] Expected temperature trajectory calculation initialization:

[0146]

[0147] where, is the measured battery temperature at the current time.

[0148] Iterative calculation: for .

[0149] Calculate the current heat power:

[0150]

[0151] The expected temperature is calculated:

[0152]

[0153] Here, the ambient temperature is constant, or a predicted ambient temperature variation curve is used.

[0154] The expected temperature trajectory output:

[0155]

[0156] wherein, is the expected temperature sequence from to .

[0157] The expected current trajectory for multi-stage constant current charging is a constant value for the expected charging current of the current stage:

[0158]

[0159] wherein, is the expected charging current of the kth step.

[0160] The expected current trajectory output:

[0161]

[0162] wherein, is the expected current sequence from to .

[0163] Step S22, according to the safety characteristics of each monitoring parameter, respectively determine the initial width value of the upper and lower boundaries thereof; the monitoring parameters at least include the terminal voltage parameter, the battery temperature parameter and the charging current parameter;

[0164] In this embodiment, the voltage parameter, the battery temperature parameter and the charging current parameter are selected as the monitoring parameters.

[0165] For the terminal voltage parameter, according to the safety characteristics that the overvoltage risk is higher than the undervoltage risk, the relationship between the upper boundary initial width value and the lower boundary initial width value is expressed as:

[0166]

[0167] wherein, is the voltage boundary proportionality coefficient, and ;

[0168] Since overvoltage of the battery can cause serious safety accidents such as electrolyte decomposition, gas production, and thermal runaway, the risk is much higher than that of under-voltage. Therefore, the upper boundary should be more stringent than the lower boundary, and be set more narrowly. Based on this, the voltage boundary proportion coefficient may be set to 0.6.

[0169] For the battery temperature parameter, according to the safety characteristics that the high-temperature risk increases with the increase of the charging current, the relationship between the initial width value of the upper boundary and the charging current value is expressed as:

[0170]

[0171] wherein, is the basic temperature boundary width value, is the temperature boundary adjustment coefficient, and ;

[0172] For the battery temperature parameter, the high-temperature risk is positively correlated with the size of the charging current. The larger the current, the faster the heat production, so the upper boundary width should be dynamically tightened with the increase of the current. Based on this, the temperature boundary adjustment coefficient can be set to 0.2.

[0173] It should be noted that, similar to the voltage parameter, the temperature boundary also has asymmetry, the upper boundary is very sensitive and dynamically tightened with the current to prevent high-temperature risk. The lower boundary is relatively loose and has a fixed narrow width, mainly used for fault detection rather than safety prevention. The asymmetric design reflects the differentiated monitoring strategy for different direction risks.

[0174] For the charging current parameter, according to the safety characteristics that there is a transient fluctuation during stage switching, the initial width value of the boundary in the stage is set, and the boundary width value at the stage switching moment is expressed as:

[0175]

[0176] wherein, is the switching coefficient.

[0177] For the charging current parameter, considering that there is an adjustment transient in the output of the charger when executing the stage switching instruction, the current fluctuation during this period is a normal phenomenon. Therefore, at the stage switching moment, the boundary is temporarily relaxed. Based on this, the switching coefficient may be set to be greater than 1, for example, it can be taken as 1.5, 1.8, etc., and can be set according to actual conditions. During the steady-state charging period in the non-switching stage, the upper and lower boundaries of the charging current are usually set to be symmetrical. At the stage switching moment, the upper and lower boundaries are multiplied by the switching coefficient Temporary relaxation is performed.

[0178] It should be noted that the boundary of the switching time is temporarily relaxed because there is a transient process such as overshoot and oscillation when the charger hardware adjusts the output current. The boundary is temporarily relaxed during switching to avoid misjudging the normal adjustment process as an abnormality. Unlike voltage and temperature, the monitoring boundary of the charging current is symmetrical in the steady state and is also symmetrical when relaxed during switching. Because both overlarge and undersize currents are abnormal states, overshoot during switching may cause the current to be higher than the target value, and undershoot may cause the current to be lower than the target value, ensuring that the charging current is stable near the preset optimal value. Both upward and downward transient fluctuations of the current can occur, so the upper and lower boundaries are symmetrically relaxed.

[0179] The initial width value of the lower boundary of each monitoring parameter is determined based on the battery specification and the charging strategy. A default value is set for the initial width value of the lower boundary of each monitoring parameter, that is:

[0180]

[0181] wherein, are default values determined based on the battery specification preset.

[0182] Although the risk of charging at low temperature is generally low, for lithium batteries, charging at extremely low temperature may cause lithium to precipitate, damaging the battery. The lower boundary can be used to identify such an abnormal low-temperature environment. The temperature sensor may have a drift, short circuit or open circuit failure, causing the temperature reading to be abnormally low. The lower boundary can be used as an early indicator of sensor failure.

[0183] By quantifying different types of risks such as overvoltage, overheating and transient interference into asymmetric boundary rules, the safety monitoring strategy is closely combined with the physical characteristics of the battery and the charging platform, improving the accuracy of risk identification.

[0184] Step S23, dynamically adjusting the initial width value based on the real-time state of charge value and the real-time temperature value of the battery, to obtain an adjusted width value;

[0185] The real-time state of charge value SOC(t) of the battery at time t is obtained, and the initial width value is dynamically adjusted based on the real-time state of charge value, which includes:

[0186] A first adjustment coefficient is determined according to the real-time state of charge value;

[0187] When the real-time state of charge value is lower than a first preset threshold, the first adjustment coefficient is set to a first value A1;

[0188] When the real-time state of charge value is between the first preset threshold and a second preset threshold, the first adjustment coefficient is set to a second value A2;

[0189] when the real-time state of charge value is higher than a second preset threshold value, setting the first adjustment coefficient as a third value A3;

[0190] wherein A1 and A3 are less than A2.

[0191] Therefore, the SOC adjustment coefficient fSOC(t) determined according to the real-time state of charge value SOC(t) is expressed as:

[0192]

[0193] wherein, , is a SOC threshold value, and satisfies 0 ≤1, and the coefficient satisfies A1

[0194] Since the polarization degree and chemical activity of the battery are different in different SOC intervals. In the low SOC (such as <20%) and high SOC (such as >80%) intervals, the battery parameters are more sensitive, so the boundary width is multiplied by a coefficient less than 1 to tighten; in the middle SOC interval, the performance is stable, and the coefficient is 1.0 and remains unchanged.

[0195] obtaining a real-time temperature value T(t) of the battery at time t, and dynamically adjusting the initial width value based on the real-time temperature value comprises:

[0196] determining a second adjustment coefficient according to the real-time temperature value;

[0197] when the real-time temperature value is lower than a third preset threshold value, setting the second adjustment coefficient as a fourth value B1;

[0198] when the real-time temperature value is between the third preset threshold value and a fourth preset threshold value, setting the second adjustment coefficient as a fifth value B2;

[0199] when the real-time temperature value is higher than the fourth preset threshold value, setting the second adjustment coefficient as a sixth value B3;

[0200] wherein B1 is greater than B2, and B3 is less than B2.

[0201] determining a temperature adjustment coefficient fT(t) according to the real-time temperature value T(t) is expressed as:

[0202]

[0203] wherein, , is a temperature threshold value, and satisfies , and the coefficient satisfies B1 ​​

[0204] The ambient temperature directly affects the battery internal resistance and the chemical reaction rate. At low temperature, the battery internal resistance increases, and the normal fluctuation range becomes wider, so a coefficient greater than 1 is multiplied to relax the boundary width; at high temperature, in order to prevent thermal runaway, a coefficient less than 1 is multiplied to tighten.

[0205] Since the initial width rule is static, and the state of the battery in actual operation is dynamically changing. This step introduces two key state variables, the real-time state of charge (SOC) and the real-time temperature of the battery, to make a secondary dynamic compensation on the boundary width, giving the tolerance corridor self-adaptive ability. Based on the real-time state of charge and the implementation temperature adjustment, this makes the safety monitoring system have the intelligence of perceiving the environment and adapting to the state. Effectively solve the problem that the fixed rule cannot adapt to the whole life cycle of the battery and the whole temperature range of the working condition. Through dynamic adjustment, it can automatically improve the monitoring sensitivity when the battery state is sensitive or the environment is bad, and avoid unnecessary false alarms when the state is stable, thereby greatly reducing the false alarm rate while ensuring the monitoring effectiveness in complex real scenarios.

[0206] Step S24, generate the final tolerance corridor boundary based on the expected parameter trajectory as the center, combined with the adjusted width value, and take it as the safety reference.

[0207] The adjusted width value is obtained by multiplying the initial width value by the first adjustment coefficient and the second adjustment coefficient. The initial width value of each monitoring parameter i is dynamically adjusted:

[0208]

[0209]

[0210] Generate the final tolerance corridor boundary based on the expected parameter trajectory as the center, using the adjusted width value: Upper boundary:

[0211] Lower boundary:

[0212] By comparing with the expected trajectory conforming to the battery mechanism, the abnormal signs of deviating from the normal change trend can be found early before the absolute value of the parameter reaches the dangerous threshold, realizing early warning. The asymmetric boundary makes the monitoring resources more concerned about the direction with higher risk, while avoiding excessive monitoring in the low-risk direction, improving the efficiency of the charging platform safety management.

[0213] Step S3, according to the monitoring parameter of the real-time running parameter, judge whether the monitoring parameter exceeds the upper and lower boundaries of the tolerance corridor;

[0214] ​

[0215] During the charging process, the charging station control system continuously acquires real-time values of each monitoring parameter at a fixed sampling frequency. For each sampling time t, the following operations are performed simultaneously:

[0216] The measured value of the current terminal voltage is obtained. The measured value of the battery temperature is obtained. The measured value of the charging current is obtained.

[0217] According to the current time t and the charging phase, the corresponding upper boundary value and lower boundary value at the same time are retrieved from the final tolerance corridor generated in step S24. The boundary values are dynamically changing, reflecting the safety threshold of the battery under different states.

[0218] The system performs the following judgment logic for each monitoring parameter in parallel. For example, for the terminal voltage parameter:

[0219] If (voltage upper boundary value), the voltage breaks through the upper boundary, recorded as a voltage upper boundary crossing event;

[0220] If (voltage lower boundary value), the voltage breaks through the lower boundary, recorded as a voltage lower boundary crossing event;

[0221] If ≤ ≤ , the voltage is within the safe range.

[0222] Similarly, for the battery temperature parameter, there are temperature upper boundary crossing events, temperature lower boundary crossing events, and temperature within the safe range; for the charging current parameter, there are current upper boundary crossing events, current lower boundary crossing events, and current within the safe range.

[0223] Step S4, if the boundary is exceeded, at least one abnormal risk mode is associated according to the type of the boundary exceeded, and a data packet corresponding to the identification of the abnormal risk mode is triggered.

[0224] By monitoring and identifying the events of each monitoring parameter exceeding the upper boundary or lower boundary of the tolerance corridor, the boundary breakthrough type is determined; specifically including:

[0225] When the terminal voltage parameter breaks through its upper boundary, the associated risk mode is overvoltage risk;

[0226] When the terminal voltage parameter breaks through its lower boundary, the associated risk mode is under-voltage or connection abnormality;

[0227] When the battery temperature parameter breaks through its upper boundary, the associated risk mode is overheat risk;

[0228] ​When the charging current parameter breaks through its upper boundary, the associated risk mode is overcurrent risk;

[0229] When the charging current parameter breaks through its lower boundary, the associated risk mode is charging interruption or communication anomaly;

[0230] Based on the mapping relationship between the preset boundary breakthrough type and the abnormal risk mode, at least one abnormal risk mode is associated for the corresponding event, and a data packet is generated and reported.

[0231] The charging platform system internally presets an abnormal mapping knowledge base, which is constructed based on battery failure mechanism analysis and a large amount of experimental data, and defines the mapping relationship from boundary breakthrough event to abnormal risk mode. When the boundary breakthrough abnormality is confirmed in step S3, the system performs the following mapping:

[0232] The voltage upper boundary breakthrough is overvoltage risk, and the formation reasons include abnormal increase of battery internal resistance, charger output out of control, BMS voltage sampling failure, battery equalization failure, etc.; the voltage lower boundary breakthrough is under-voltage or connection abnormality, and the formation reasons include abnormal increase of battery internal resistance, charger output out of control, BMS voltage sampling failure, battery equalization failure, etc.

[0233] The temperature upper boundary breakthrough is over-temperature risk, and the formation reasons include heat dissipation system failure, high environmental temperature, excessive charging current, battery internal short circuit, thermal management system failure, etc.; the temperature lower boundary breakthrough is temperature sensor abnormality, and the formation reasons include temperature sensor failure, connection line disconnection, sampling circuit abnormality, etc.

[0234] The current upper boundary breakthrough is overcurrent risk, and the formation reasons include charger control loop abnormality, current sampling deviation, load mutation, etc.; the current lower boundary breakthrough is charging interruption, and the formation reasons include communication interruption, accidental pulling out of the charging gun, protection device action, power interruption, etc.

[0235] Each risk mode identifier is composed of a risk type and a specific parameter name. For overvoltage tendency risk, a plan of reducing the charging current can be executed; for over-temperature tendency risk, a plan of switching to a lower current stage or suspending charging can be executed; for connection type risk, a plan of temporarily interrupting charging and re-performing handshake communication can be executed. After the plan is executed, it is monitored whether the key parameters return to the tolerance corridor, and this monitoring result is reported as a plan effect index.

[0236] The data packet includes abnormal information, charging context information and device identification information. The abnormal information includes risk mode identifier, breakthrough boundary type, occurrence timestamp; the charging context information includes current charging stage, current charging current value, battery real-time SOC; the device identification information includes charging station ID and charging pile port number.

[0237] Specifically, according to the abnormal risk mode, the reporting priority of the data packet is adjusted; wherein the reporting priority is divided according to the risk degree of the abnormal risk mode to the battery safety. Wherein the voltage abnormality is given the highest reporting priority. According to the set reporting priority, the abnormality with high priority is reported immediately. When the communication is interrupted, the reporting information is stored locally in order of priority, and after the communication is restored, the reporting information is re-sent in order of priority.

[0238] It should be noted that when the breakthrough event of the same boundary type occurs continuously in the same charging stage, the trend of the breakthrough amplitude and the duration is analyzed; if the breakthrough amplitude shows an expanding trend or the duration exceeds the preset proportion of the stage length, the risk mode identifier is upgraded to an enhanced risk prompt of trend deterioration; if multiple different parameters break through the same boundary type successively in the same charging cycle, a systematic overload risk warning is generated.

[0239] Based on the optimal current value of the current charging stage, the application calculates the expected parameter trajectory of each parameter through a second-order RC equivalent circuit model, and generates a tolerance corridor centered on it, rather than relying on fixed numerical thresholds. It fully meets the dynamic change rule of parameters in multi-stage constant current charging, and reduces the false positive rate and false negative rate of abnormal monitoring.

[0240] At the same time, there are essential differences in the safety risk levels of different monitoring parameters. The application differentiates the upper and lower boundary widths according to the safety characteristics of each parameter through tolerance corridor design. This design enables the tolerance corridor to match the SOC state and temperature environment of the battery in real time, regardless of whether the battery is a new battery or an aged battery, or the charging stage or environmental temperature, and can maintain stable monitoring accuracy, solving the pain point of poor adaptability of traditional fixed thresholds.

[0241] In addition, two-wheel electric vehicles in public charging stations generally have complex scenarios such as no active cooling, various battery brands, and different aging degrees. The application does not need to rely on additional cooling equipment, and can adapt to batteries of different brands and different aging degrees through dynamically adjusted tolerance corridors; at the same time, the tolerance design and dynamic adjustment mechanism are based on the general characteristics of the battery, without the need to modify the core charging hardware of the charging station or use high-power hardware configuration based on artificial intelligence for abnormal monitoring. It can be realized through lightweight software algorithm optimization, reducing the cost of technology landing and having strong universality and engineering application value.

[0242] Specifically, in one preferred embodiment of the application, the environmental temperature and humidity of the environment where the charging station is located are obtained, and the boundaries of the tolerance corridor are relaxed or tightened according to the environmental temperature and humidity. The specific implementation process is as follows:

[0243] The environmental temperature value of the environment where the charging station is located is obtained in real time through the environmental sensor configured by the charging station with an environmental humidity value .

[0244] with the environmental temperature value , querying a preset temperature-compensation coefficient mapping table to determine a temperature-compensation coefficient ; the mapping table satisfies:

[0245] when , ; when , ; when , ; wherein, is a preset low-temperature threshold, is a preset high-temperature threshold.

[0246] with the environmental humidity value , querying a preset humidity-compensation coefficient mapping table to determine a humidity-compensation coefficient ; the mapping table satisfies:

[0247] when , ; when , ; when , ; wherein, is a preset low-humidity threshold, is a preset high-humidity threshold.

[0248] calculating a comprehensive compensation coefficient:

[0249]

[0250] multiplying the upper boundary width value and the lower boundary width value of the tolerance corridor by the comprehensive compensation coefficient , to obtain compensated boundary width values:

[0251]

[0252]

[0253] centering on the expected parameter trajectory , using the compensated boundary width values and , to generate a compensated tolerance corridor boundary representation as:

[0254]

[0255]

[0256] In this embodiment, the voltage boundary is appropriately relaxed in a low-temperature environment, and the temperature boundary is appropriately tightened in a high-temperature environment; the charging current boundary is compensated according to the environmental humidity, and the current boundary is appropriately tightened in a high-humidity environment to reduce the risk of condensation. By introducing the environmental temperature and humidity compensation mechanism, the charging safety monitoring can perceive and respond to changes in external climate conditions. The false alarm or missed alarm situation in extreme weather is avoided, the adaptability of the public charging station in complex outdoor environments is improved, and the risk of battery thermal runaway caused by environmental factors is reduced.

[0257] Please refer to Fig. 3 The application embodiment also provides a charging station charging abnormality automatic reporting system for the foregoing method, comprising:

[0258] A data acquisition unit is configured to acquire real-time operation parameters of the battery in a multi-stage constant-current charging process.

[0259] A safety boundary generation unit is configured to determine a current state of the battery according to the real-time operation parameters, and generate a tolerance corridor serving as a safety reference based on the current state of the battery, wherein the tolerance corridor defines upper and lower boundaries allowing fluctuations of real-time parameters with an expected parameter trajectory as a center, and the widths of the upper and lower boundaries are set to be different from each other according to the parameter type and the charging stage.

[0260] A judgment unit is configured to judge whether a monitoring parameter of the real-time operation parameters exceeds the upper and lower boundaries of the tolerance corridor.

[0261] An abnormality reporting unit is configured to, if the boundary is exceeded, associate at least one abnormality risk mode according to the type of the exceeded boundary, and trigger reporting of a data packet corresponding to an identifier of the abnormality risk mode.

[0262] The functions of each unit in this embodiment are the same as those of the charging station charging abnormality automatic reporting method, and the technical effects are the same, which will not be repeated here.

[0263] Those skilled in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components of each example have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0264] In the embodiments provided by the present application, it should be understood that the division of the units is merely a logical functional division, and in actual implementation, another division manner can be used, for example, a plurality of units can be combined into one unit, one unit can be split into a plurality of units, or some features can be ignored, etc.

[0265] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0266] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0267] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the specification of the present application.

Claims

1. A method for automatically reporting charging abnormalities of a charging station, applied to a multi-stage constant current charging scenario of a battery of a two-wheeled electric vehicle, characterized in that, The method comprises: acquiring real-time operation parameters of the battery during a multi-stage constant current charging process; determining a current state of the battery according to the real-time operation parameters, and generating a tolerance corridor as a safety benchmark based on the current state of the battery, the tolerance corridor defining upper and lower boundaries allowing fluctuations of the real-time parameters around an expected parameter trajectory, and the width of the upper and lower boundaries being set according to the type of the parameters; judging whether a monitored parameter of the real-time operation parameters exceeds the upper and lower boundaries of the tolerance corridor; if the boundary is exceeded, associating at least one abnormal risk mode according to the type of the exceeded boundary, and triggering the reporting of a data packet corresponding to the identification of the abnormal risk mode; the generating of the tolerance corridor as a safety benchmark based on the current state of the battery comprises: calculating the expected parameter trajectory of each monitored parameter using a second-order RC equivalent circuit model of the battery based on the optimal charging current value of the current charging stage; determining the initial width values of the upper and lower boundaries of each monitored parameter according to the safety characteristics of the monitored parameters; the monitored parameters at least include an end voltage parameter, a battery temperature parameter, and a charging current parameter; dynamically adjusting the initial width values based on the real-time state of charge value and the real-time temperature value of the battery to obtain adjusted width values; generating the final tolerance corridor boundaries as a safety benchmark by taking the expected parameter trajectory as the center and combining the adjusted width values; the determining of the initial width values of the upper and lower boundaries of each monitored parameter according to the safety characteristics of the monitored parameters comprises: for the end voltage parameter, the initial width of the upper boundary is proportionally narrower than that of the lower boundary because the overvoltage risk is higher than the undervoltage risk; for the temperature parameter, the initial width of the upper boundary is linearly tightened with the charging current because the temperature rise risk increases with the current; for the current parameter, the boundary width is relaxed by a coefficient times at the switching time because there is transient fluctuation at the stage switching, and the upper and lower boundaries are symmetrically arranged; wherein the initial width of the lower boundary of each monitored parameter is pre-set as a fixed value based on the battery specifications and the charging strategy.

2. The method according to claim 1, wherein, The method further comprises: acquiring the ambient temperature and the ambient humidity of the environment where the charging station is located, and relaxing or tightening the boundaries of the tolerance corridor according to the ambient temperature and the ambient humidity.

3. The method of claim 1, wherein the method further comprises: The dynamic adjustment of the initial width values based on the real-time state of charge value comprises: determining a first adjustment coefficient according to the real-time state of charge value; when the real-time state of charge value is lower than a first preset threshold, setting the first adjustment coefficient as a first value A1; when the real-time state of charge value is between the first preset threshold and a second preset threshold, setting the first adjustment coefficient as a second value A2; when the real-time state of charge value is higher than the second preset threshold, setting the first adjustment coefficient as a third value A3; wherein A1 and A3 are less than A2.

4. The method according to claim 3, characterized in that, The dynamic adjustment of the initial width values based on the real-time temperature value comprises: determining a second adjustment coefficient according to the real-time temperature value; when the real-time temperature value is lower than a third preset threshold, setting the second adjustment coefficient as a fourth value B1; when the real-time temperature value is between the third preset threshold and a fourth preset threshold, setting the second adjustment coefficient as a fifth value B2; When the real-time temperature value is higher than a fourth preset threshold value, the second adjustment coefficient is set as a sixth value B3; Wherein, B1 is greater than B2, and B3 is less than B2.

5. The method of claim 4, wherein the method further comprises: Further comprising: Multiplying the initial width value by the first adjustment coefficient and the second adjustment coefficient to obtain an adjusted width value.

6. The method of claim 1, wherein the method further comprises: If the boundary is exceeded, at least one abnormal risk mode is associated according to the type of the exceeded boundary, and a data packet corresponding to the identification of the abnormal risk mode is triggered to be reported, including: Monitoring and identifying events in which each monitoring parameter exceeds the upper or lower boundary of the tolerance corridor, and determining the boundary breakthrough type; Based on the mapping relationship between the preset boundary breakthrough type and the abnormal risk mode, at least one abnormal risk mode is associated for the corresponding event, a data packet is generated and reported.

7. The method according to claim 6, characterized in that, Further comprising: According to the abnormal risk mode, the reporting priority of the data packet is adjusted; wherein the reporting priority is divided according to the risk degree of abnormal risk mode to the safety of the battery.

8. A charging station charging anomaly automatic reporting system for implementing the method of any one of claims 1-7, characterized in that, Including: A data acquisition unit is configured to acquire real-time operating parameters of a battery during a multi-stage constant current charging process; A safety boundary generation unit is configured to determine the current state of the battery according to the real-time operating parameters, and generate a tolerance corridor serving as a safety benchmark based on the current state of the battery, wherein the tolerance corridor defines upper and lower boundaries that allow real-time parameters to fluctuate around an expected parameter trajectory, and the width of the upper and lower boundaries is set to be different according to the parameter type and the charging stage; A judgment unit is configured to judge whether the monitoring parameters of the real-time operating parameters exceed the upper and lower boundaries of the tolerance corridor; An abnormality reporting unit is configured to, if the boundary is exceeded, associate at least one abnormal risk mode according to the type of the exceeded boundary, and trigger the reporting of a data packet corresponding to the identification of the abnormal risk mode.

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