Method and system for automatically reporting charging abnormity of charging station

Abstract

The invention discloses a charging station charging abnormity automatic reporting method and system, is applied to a multi-stage constant current charging scene of a two-wheeled electric vehicle battery, and relates to the technical field of charging safety, and the method comprises the steps: obtaining real-time operation parameters of the battery in a multi-stage constant current charging process; the current state of the battery is determined according to the real-time operation parameters, and a tolerance corridor serving as a safety reference is generated based on the current state of the battery; according to the monitoring parameters of the real-time operation parameters, judging whether the monitoring parameters exceed the upper and lower boundaries of the tolerance corridor; and if the boundary is exceeded, associating at least one abnormal risk mode according to the exceeded boundary type, and triggering to report a data packet corresponding to the abnormal risk mode identifier. According to the method, the tolerance corridor is dynamically generated based on the current state of the battery, parameter characteristic differences of different charging stages are fully considered, the problems of false alarm and missing alarm caused by a traditional fixed threshold value are avoided, and abnormal judgment is deeply matched with the dynamic process of multi-stage constant-current charging.

Description

Technical Field

[0001] This invention relates to the field of battery charging safety technology, and in particular to a method and system for automatically reporting charging anomalies at charging stations. Background Technology

[0002] With the rapid popularization of new energy transportation systems, two-wheeled electric vehicles have become a core mode of transportation for short-distance commuting and last-mile delivery in cities due to their flexibility, convenience, and low-carbon economy. As some cities have a large number of two-wheeled electric vehicles, the demand for public smart charging stations has surged. However, public charging stations are mostly located in densely populated areas such as communities, commercial districts, and delivery stations, and the charging scenarios are primarily characterized by a lack of active cooling. Furthermore, the diverse brands and varying degrees of aging of two-wheeled electric vehicle batteries make them prone to safety accidents such as thermal runaway and short circuits during charging due to voltage fluctuations, abnormal temperatures, and sudden current changes.

[0003] In the field of public charging for two-wheeled electric vehicles, existing technologies propose multi-stage constant current charging control methods with different optimization directions. By adjusting the charging current at each stage, charging efficiency and basic safety are improved. Examples include conventionally optimized uniform multi-stage constant current charging control strategies and the improved PSO algorithm multi-stage constant current charging optimization strategy proposed in publication CN120828698A. However, existing technologies have shortcomings in the anomaly monitoring stage of multi-stage constant current charging. Traditional anomaly monitoring often uses fixed threshold judgment methods, which are not adapted to the optimized control model of multi-stage constant current charging. It does not consider the differences in current and voltage characteristics at different stages of multi-stage constant current charging, nor the dynamic impact of battery state of charge and temperature on the safe range of parameters. This easily leads to false alarms or missed alarms due to normal fluctuations, resulting in insufficient monitoring accuracy. To address the shortcomings of existing technologies, there is an urgent need for a technical solution that can adapt to the characteristics of multi-stage constant current charging, accurately identify and report anomaly risks, and bridge the gap between existing charging control and safety monitoring.

[0004] Therefore, a method and system for automatically reporting charging anomalies at charging stations is needed. Summary of the Invention

[0005] To address the problem that existing technologies often fail to adapt anomaly detection to the optimized control model of multi-stage constant current charging, leading to false alarms or missed alarms due to misinterpreting normal fluctuations as anomalies and resulting in insufficient monitoring accuracy, this invention provides an automatic charging anomaly reporting method and system for charging stations. Applied to multi-stage constant current charging scenarios for two-wheeled electric vehicle batteries, this method dynamically generates a tolerance corridor based on the current battery state, fully considering the differences in parameter characteristics at different charging stages. This avoids the false alarms and missed alarms caused by traditional fixed thresholds, ensuring deep adaptation of anomaly detection to the dynamic process of multi-stage constant current charging. The specific technical solution is as follows: In a first aspect, the present invention provides a method for automatically reporting charging anomalies at charging stations, applicable to multi-stage constant current charging scenarios for two-wheeled electric vehicle batteries, including: Acquire real-time operating parameters of the battery during the multi-stage constant current charging process; The current state of the battery is determined based on the real-time operating parameters. A tolerance corridor is generated as a safety benchmark based on the current state of the battery. The tolerance corridor defines the upper and lower boundaries centered on the expected parameter trajectory and allowing real-time parameter fluctuations. The width of the upper and lower boundaries is set according to the parameter type. Based on the monitoring parameters of the real-time operating parameters, determine whether the monitoring parameters exceed the upper and lower boundaries of the tolerance corridor; If the boundary is exceeded, at least one abnormal risk mode is associated with the type of boundary exceeded, and a data packet corresponding to the abnormal risk mode identifier is reported.

[0006] Preferably, generating a tolerance corridor as a safety benchmark based on the current state of the battery includes: Based on the optimal charging current value at the current charging stage, the expected parameter trajectory of each monitoring parameter is calculated using the second-order RC equivalent circuit model of the battery. Based on the safety characteristics of each monitoring parameter, the initial width values ​​of its upper and lower boundaries are determined respectively; the monitoring parameters include at least the terminal voltage parameter, battery temperature parameter, and charging current parameter; Based on the real-time state of charge and real-time temperature of the battery, the initial width value is dynamically adjusted to obtain the adjusted width value. Centered on the expected parameter trajectory, the final tolerance corridor boundary is generated by combining the adjusted width value and used as a safety benchmark.

[0007] Preferably, a method for automatically reporting charging anomalies at a charging station further includes: The ambient temperature and humidity of the charging station's environment are obtained, and the boundary of the tolerance corridor is widened or tightened based on the ambient temperature and humidity.

[0008] Preferably, determining the initial width values ​​of the upper and lower boundaries of each monitoring parameter based on their safety characteristics includes: For the terminal voltage parameter, based on the fact that the risk of overvoltage is higher than that of undervoltage, the initial width of its upper boundary is proportionally narrower than that of its lower boundary. Regarding the temperature parameter, based on the principle that the risk of temperature rise increases with current, the initial width of its upper boundary tightens linearly with the charging current. For the current parameters, considering the transient fluctuations that occur during stage switching, the boundary width is widened by a factor at the switching moment, and the upper and lower boundaries are set symmetrically. The initial width of the lower boundary of each monitoring parameter is preset to a fixed value based on battery specifications and charging strategy.

[0009] Preferably, the initial width values ​​of the lower boundaries of each monitoring parameter are determined based on the battery specifications and the charging strategy, respectively.

[0010] Preferably, dynamically adjusting the initial width value based on the real-time state of charge value includes: The first adjustment coefficient is determined based on the real-time state of charge value; When the real-time state of charge value is lower than the first preset threshold, the first adjustment coefficient is set to the first value A1; When the real-time state of charge value is between the first preset threshold and the second preset threshold, the first adjustment coefficient is set to the second value A2; When the real-time state of charge value is higher than the second preset threshold, the first adjustment coefficient is set to the third value A3; In this case, both A1 and A3 are less than A2.

[0011] Preferably, dynamically adjusting the initial width value based on the real-time temperature value includes: The second adjustment factor is determined based on the real-time temperature value; When the real-time temperature value is lower than the third preset threshold, the second adjustment coefficient is set to the fourth value B1; When the real-time temperature value is between the third preset threshold and the fourth preset threshold, the second adjustment coefficient is set to the fifth value B2; When the real-time temperature value is higher than the fourth preset threshold, the second adjustment coefficient is set to the sixth value B3; Among them, B1 is greater than B2, and B3 is less than B2.

[0012] Preferably, a method for automatically reporting charging anomalies at charging stations further includes: The adjusted width value is obtained by multiplying the initial width value by the first adjustment coefficient and the second adjustment coefficient.

[0013] Preferably, if the boundary is exceeded, at least one abnormal risk pattern is associated with the type of the exceeded boundary, and a data packet corresponding to the abnormal risk pattern identifier is triggered for reporting, including: Monitor and identify events in which each monitored parameter exceeds the upper or lower boundary of the tolerance corridor, and determine the boundary breach type; Based on the preset mapping relationship between boundary breach types and abnormal risk patterns, at least one abnormal risk pattern is associated with the corresponding event, and a data packet is generated and reported.

[0014] Preferably, a method for automatically reporting charging anomalies at a charging station further includes: The reporting priority of the data packets is adjusted according to the abnormal risk pattern; wherein the reporting priority is divided according to the degree of risk to battery safety based on the abnormal risk pattern.

[0015] Secondly, the present invention also provides an automatic reporting system for charging station charging anomalies, used to implement the aforementioned method, comprising: The data acquisition unit is used to acquire the real-time operating parameters of the battery during the multi-stage constant current charging process; A safety boundary generation unit is used to determine the current state of the battery based on the real-time operating parameters, and generate a tolerance corridor as a safety benchmark based on the current state of the battery. The tolerance corridor defines upper and lower boundaries centered on the expected parameter trajectory, allowing real-time parameter fluctuations, and the width of the upper and lower boundaries is set to be different for each other according to the parameter type and charging stage. The judgment unit is used to determine whether the monitoring parameters of the real-time operating parameters exceed the upper and lower boundaries of the tolerance corridor based on the monitoring parameters of the real-time operating parameters. An anomaly reporting unit is used to, if a boundary is exceeded, associate at least one anomaly risk mode with the type of boundary exceeded, and trigger the reporting of a data packet corresponding to the anomaly risk mode identifier.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides an automatic charging anomaly reporting method for charging stations. By dynamically generating a tolerance corridor based on the current battery state, it fully considers the differences in parameter characteristics at different charging stages, deeply adapting anomaly judgment to the dynamic process of multi-stage constant current charging. This avoids the false alarm and missed alarm problems caused by traditional fixed thresholds. More specifically, based on the optimal current value of the current charging stage, the expected parameter trajectory of each parameter is calculated using a second-order RC equivalent circuit model, and the final tolerance corridor is generated around this. This fully conforms to the dynamic change law of parameters in multi-stage constant current charging, avoiding threshold judgment defects and reducing the false alarm and missed alarm rates of anomaly monitoring. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a flowchart of an automatic reporting method for charging station charging abnormalities according to an embodiment of the present invention.

[0019] Figure 2 This is a flowchart of the tolerance corridor generation method according to an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of an automatic charging anomaly reporting system for a charging station according to an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be understood that, when used in this specification, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0023] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should also be further understood that the term "and / or" as used in this specification refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0025] Please refer to the following examples. Figures 1 to 3 .

[0026] Traditional charging anomaly monitoring often uses fixed threshold judgment methods, which are not adapted to the optimized control model of multi-stage constant current charging. It fails to consider the differences in current and voltage characteristics at different stages of multi-stage constant current charging, as well as the dynamic impact of battery state of charge and temperature on the safe range of parameters. This easily leads to misjudging fluctuations in normal stages as anomalies, resulting in false alarms or missed alarms and insufficient monitoring accuracy. While some existing methods combine artificial intelligence for anomaly monitoring in multi-stage constant current charging, the compact layout of charging stations for two-wheeled electric vehicles lacks high-performance computing hardware, making high-performance monitoring and analysis of multi-segment charging of two-wheeled electric vehicles unsuitable. For further details, please refer to this application. Figure 1 This application provides a method for automatically reporting charging anomalies at charging stations, applicable to multi-stage constant current charging scenarios for two-wheeled electric vehicle batteries, including the following steps: Step S1: Obtain the real-time operating parameters of the battery during the multi-stage constant current charging process; In this embodiment, a local controller can be set up at the charging station to interact with the battery management system (BMS) and the charging module. After charging begins, the charging station controller obtains real-time operating parameters from the BMS and the charging module via a CAN bus or a dedicated communication interface at a set sampling frequency. These real-time operating parameters include electrical parameters such as battery terminal voltage and charging current, thermal parameters such as battery surface temperature, state parameters such as battery state of charge (SOC) and state of health (SOH), and charging strategy parameters such as the current charging stage number and the preset optimal charging current value for that stage.

[0027] In other embodiments, parameters are collected for the batteries of two-wheeled electric vehicles connected to the charging piles of the charging station using corresponding sensors. For example, a voltage sensor connected in series in the output circuit of the charging pile collects the real-time voltage across the positive and negative terminals of the battery; the sensor is connected to the positive and negative terminals of the battery; an NTC (negative temperature coefficient thermistor) attached to the middle of the battery casing collects the battery surface temperature; a Hall current sensor connected in series in the output circuit of the charging pile collects the real-time charging current, adapting to the current adjustment range of multi-stage constant current charging; and the real-time SOC value is read through the battery management system (BMS). If the battery does not have a BMS, the SOC value is estimated based on the charging capacity and voltage change trend using the built-in algorithm of the charging pile. Simultaneously, the collected raw data is filtered to remove abnormal spikes caused by electromagnetic interference, retaining the true operating parameters.

[0028] Meanwhile, to ensure data quality, the collected raw data is filtered and outlier removal preprocessed, and the preprocessed real-time parameters are transmitted to the charging station management platform in real time via 4G / 5G modules.

[0029] Step S2: Determine the current state of the battery based on the real-time operating parameters, and generate a tolerance corridor as a safety benchmark based on the current state of the battery. The tolerance corridor defines the upper and lower boundaries centered on the expected parameter trajectory and allowing real-time parameter fluctuations, and the width of the upper and lower boundaries is set according to the parameter type. A personalized tolerance corridor for a safety baseline is dynamically generated for each key monitoring parameter. This tolerance corridor not only reflects the theoretically expected trajectory of the parameter but also, through asymmetric boundary settings, reflects the risk differences in different directions. (See also...) Figure 2 Generating a tolerance corridor as a safety benchmark based on the current state of the battery includes: Step S21: Based on the optimal charging current value of the current charging stage, calculate the expected parameter trajectory of each monitoring parameter using the second-order RC equivalent circuit model of the battery. The parameter table of the second-order RC model corresponding to this battery model was loaded. This table was identified through HPPC (Hybrid Pulse Power Characteristic Test) experiments and stores the model parameters at different SOC points: ohmic internal resistance, electrochemical polarization resistance and capacitance, concentration polarization resistance and capacitance, and open-circuit voltage.

[0030] 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: 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 .

[0031] Obtain the current parameter from the pre-stored second-order RC model parameter table using bilinear interpolation. and The following model parameters: 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; State variable initialization (extended Kalman filter) state vector definition: in, This is the electrochemical polarization voltage. This is the concentration polarization voltage.

[0032] EKF prediction steps: in, For the prior state estimation at step k, To estimate the error covariance matrix a priori, Here is the state transition matrix. For the input matrix, Input to the system (charging current). The process noise covariance matrix; EKF update steps: In the formula, Here is the Kalman gain matrix. For the observation matrix, These are actual observed values ​​(terminal voltage measurements). To observe the noise covariance matrix, For the posterior state estimation at step k, This is the posterior estimation error covariance matrix.

[0033] State transition matrix : Input matrix : in, For Coulomb efficiency, The sampling period is Convert Ah to As (coulomb).

[0034] Observation matrix : Observation equation: In the formula, This is the open-circuit voltage corresponding to the current SOC. Let the charging current be the charging current at step k. For ohmic internal resistance, and These are the electrochemical polarization voltage and concentration polarization voltage at step k, respectively.

[0035] Through EKF iteration, the optimal estimate of the state variable at the current moment is obtained: From the current moment Until the end of the current charging phase The time interval is State equation: In the formula, , , These are the derivatives of SOC, electrochemical polarization voltage, and concentration polarization voltage with respect to time, respectively. For Coulomb efficiency; This is the optimal charging current.

[0036] For each time step ,in , : SOC Update: In the formula, and These are the SOC values ​​for the k-th step and the (k+1)-th step, respectively.

[0037] Polarization voltage update: In the formula, , These are the electrochemical polarization voltage and concentration polarization voltage at step k, respectively; , , , These are the model parameters corresponding to the k-th step.

[0038] Model parameter update: In the formula, , , , , , This is a function that retrieves model parameters through table lookup and interpolation. Let be the battery temperature at step k.

[0039] Expected terminal voltage calculation: In the formula, This represents the expected terminal voltage at step k+1.

[0040] Expected terminal voltage trajectory output: in, From arrive The expected terminal voltage sequence.

[0041] Thermal model parameters: Battery thermal parameters include battery mass. Battery specific heat capacity Battery surface area convective heat transfer coefficient Thermal time constant ; Total heat generation during charging include: Ohm's heat: Polarization heat: Reversible heat (heat of electrochemical reaction): Total heat production capacity: in, Temperature coefficient of open-circuit voltage; , , These are the ohmic internal resistance, electrochemical polarization resistance, and concentration polarization resistance at time t, respectively. Let t be the battery temperature at time t.

[0042] Heat dissipation power calculation: In the formula, Let t be the ambient temperature at time t.

[0043] Temperature dynamic equation: Continuous-time equation In the formula, This is the derivative of battery temperature with respect to time.

[0044] Discrete-time equations (Euler method): In the formula, and These are the battery temperatures at step k and step (k+1), respectively.

[0045] Initialization of expected temperature trajectory calculation: In the formula, This is the battery temperature measured at the current moment.

[0046] Iterative computation: For .

[0047] Calculate the current heat production capacity: Calculate the expected temperature: Here, the ambient temperature Constant, or use a predicted ambient temperature change curve.

[0048] Expected temperature trajectory output: in, From arrive The expected temperature sequence.

[0049] For multi-stage constant current charging, the expected charging current trajectory is a constant value for the current stage: In the formula, This represents the expected charging current at step k.

[0050] Expected current trajectory output: in, From arrive The expected current sequence.

[0051] Step S22: Determine the initial width values ​​of the upper and lower boundaries of each monitoring parameter according to their safety characteristics; the monitoring parameters include at least the terminal voltage parameter, battery temperature parameter, and charging current parameter. In this embodiment, voltage parameters, battery temperature parameters, and charging current parameters are selected as monitoring parameters.

[0052] For the terminal voltage parameter, based on its safety characteristic that the risk of overvoltage is higher than the risk of undervoltage, the initial width value of the upper boundary is set... With the initial width value of the lower boundary The relationship is represented as: in, This is the voltage boundary scaling factor, and ; Because overvoltage can lead to serious safety incidents such as electrolyte decomposition, gas production, and even thermal runaway, its risks are far greater than those of undervoltage. Therefore, the upper boundary should be more stringent and narrower than the lower boundary. Based on this, the voltage boundary scaling factor... It can be set to 0.6.

[0053] Regarding battery temperature parameters, based on the safety characteristic that the risk of high temperature increases with increasing charging current, the initial width value of the upper boundary is determined. With charging current value The relationship is represented as: in, The base temperature boundary width value, This is the temperature boundary adjustment coefficient, and ; Regarding battery temperature parameters, the risk of high temperature is positively correlated with the charging current. The higher the current, the faster the heat generation; therefore, the upper boundary width should be dynamically tightened as the current increases. Based on this, the temperature boundary adjustment coefficient can be set to 0.2.

[0054] It should be noted that, similar to the voltage parameters, the temperature boundary is also asymmetric. The upper boundary is highly sensitive and dynamically tightens with increasing current to prevent high-temperature risks. The lower boundary is relatively loose, with a fixed narrow width, primarily used for fault detection rather than safety precautions. This asymmetric design reflects a differentiated monitoring strategy for risks in different directions.

[0055] For the charging current parameter, based on the safety characteristic of transient fluctuations during phase switching, an initial width value for the boundary within the phase is set. and at the stage switching time Boundary width value Represented as: in, This is the switching coefficient.

[0056] Regarding the charging current parameter, considering the transient adjustment in the charger's output during the phase switching command, current fluctuations during this period are normal. Therefore, the boundaries are temporarily relaxed at the phase switching moment. Based on this, the switching coefficient... It can be set to a value greater than 1, for example, 1.5, 1.8, etc., depending on the actual situation. During steady-state charging in the non-switching phase, the upper and lower boundaries of the charging current are usually set symmetrically. At the stage switching moment, both the upper and lower boundaries are multiplied by the switching coefficient. Temporary relaxation will be implemented.

[0057] It's important to note that the switching boundary is temporarily relaxed because the charger hardware inevitably experiences transient processes such as overshoot and oscillation when adjusting the output current. Temporarily relaxing the boundary during switching prevents normal adjustment processes from being misjudged as abnormal. Unlike voltage and temperature, the monitoring boundary for charging current is symmetrically relaxed in steady state and also symmetrically relaxed during switching. This is because both excessively high and low current are abnormal states. Overshoot during switching may cause the current to exceed the target value, while undershoot may cause the current to fall below the target value. Ensuring the charging current remains stable near the preset optimal value is crucial. Both upward and downward transient fluctuations in current can occur; therefore, the upper and lower boundaries are symmetrically relaxed.

[0058] The initial width values ​​of the lower boundary of each monitoring parameter are determined based on the battery specifications and charging strategy. Default values ​​are set for the initial width values ​​of the lower boundary of each monitoring parameter, i.e.: in, These are the default values ​​determined based on the battery specifications.

[0059] While the safety risks of charging at low temperatures are generally low, for lithium batteries, charging at extremely low temperatures can trigger lithium deposition, damaging the battery. The lower boundary can be used to identify such abnormally low temperature environments. Temperature sensors may experience drift, short circuits, or open circuit failures, resulting in abnormally low temperature readings. The lower boundary can serve as an early indicator of sensor failure.

[0060] By quantifying different types of risks such as overvoltage, overheating, and transient interference into asymmetric boundary rules, the safety monitoring strategy is closely integrated with the physical characteristics of the battery and the charging platform, thereby improving the accuracy of risk identification.

[0061] Step S23: Based on the real-time state of charge and real-time temperature of the battery, dynamically adjust the initial width value to obtain the adjusted width value; Obtaining the real-time state of charge (SOC) value of the battery at time t, and dynamically adjusting the initial width value based on the real-time SOC value includes: The first adjustment coefficient is determined based on the real-time state of charge value; When the real-time state of charge value is lower than the first preset threshold, the first adjustment coefficient is set to the first value A1; When the real-time state of charge value is between the first preset threshold and the second preset threshold, the first adjustment coefficient is set to the second value A2; When the real-time state of charge value is higher than the second preset threshold, the first adjustment coefficient is set to the third value A3; In this case, both A1 and A3 are less than A2.

[0062] Therefore, the SOC adjustment coefficient fSOC(t) determined based on the real-time state of charge value SOC(t) is expressed as: in, , The SOC threshold is given, and 0 ≤ < ≤1, coefficients satisfying A1, A3 <A2。

[0063] Because the internal polarization and chemical activity of a battery differ across different SOC ranges, battery parameters are more sensitive in the low SOC (e.g., <20%) and high SOC (e.g., >80%) ranges. Therefore, the boundary width is tightened by multiplying it by a coefficient less than 1. In the intermediate SOC range, performance is stable, and the coefficient remains unchanged at 1.0.

[0064] Obtaining the 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 includes: The second adjustment factor is determined based on the real-time temperature value; When the real-time temperature value is lower than the third preset threshold, the second adjustment coefficient is set to the fourth value B1; When the real-time temperature value is between the third preset threshold and the fourth preset threshold, the second adjustment coefficient is set to the fifth value B2; When the real-time temperature value is higher than the fourth preset threshold, the second adjustment coefficient is set to the sixth value B3; Among them, B1 is greater than B2, and B3 is less than B2.

[0065] The temperature adjustment coefficient fT(t) is determined based on the real-time temperature value T(t) and is expressed as follows: in, , It is the temperature threshold, and satisfies < The coefficients satisfy B1>B2>B3.

[0066] Ambient temperature directly affects battery internal resistance and chemical reaction rate. At low temperatures, battery internal resistance increases and the normal fluctuation range widens, so the boundary width is broadened by multiplying it by a coefficient greater than 1; at high temperatures, to prevent thermal runaway, it is tightened by multiplying it by a coefficient less than 1.

[0067] Since the initial width rule is static, while the battery's state changes dynamically during actual operation, this step introduces two key state variables—the battery's real-time state of charge (SOC) and real-time temperature—to perform secondary dynamic compensation on the boundary width, endowing the tolerance corridor with adaptive capability. Based on the battery's real-time SOC and implementing temperature adjustments, this enables the safety monitoring system to intelligently perceive the environment and adapt to its state. It effectively solves the problem that fixed rules cannot adapt to the entire battery lifecycle and the entire operating temperature range. Through dynamic adjustment, it can automatically improve monitoring sensitivity when the battery state is sensitive or the environment is harsh, and avoid unnecessary false alarms when the state is stable. This significantly reduces the false alarm rate while ensuring monitoring effectiveness in complex real-world scenarios.

[0068] Step S24: Using the expected parameter trajectory as the center, and combining it with the adjusted width value, generate the final tolerance corridor boundary, and use it as a safety benchmark.

[0069] 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 for each monitoring parameter i is dynamically adjusted. With expected parameter trajectory Centered on the center, the final tolerance corridor boundary is generated using the adjusted width value: Upper boundary: Lower boundary: By comparing the trajectory with the expected trajectory consistent with battery mechanisms, early detection of abnormal trends deviating from normal changes can be achieved before the absolute value of parameters reaches the danger threshold, enabling early warning. The asymmetric boundary allows monitoring resources to focus more on higher-risk areas while avoiding over-monitoring of low-risk areas, thus improving the efficiency of charging platform safety management.

[0070] Step S3: Based on the monitoring parameters of the real-time operating parameters, determine whether the monitoring parameters exceed the upper and lower boundaries of the tolerance corridor; During the charging process, the charging station control system continuously collects real-time values ​​of various monitoring parameters at a fixed sampling frequency. For each sampling time t, the following operations are performed simultaneously: Get current terminal voltage Battery temperature Charging current The measured value.

[0071] Based on the current time t and the charging stage, the upper and lower boundary values ​​corresponding to the same moment 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.

[0072] The system executes the following decision logic in parallel for each monitored parameter. For example, for the terminal voltage parameter: if If the voltage exceeds the upper boundary value, it is recorded as a voltage over-bounds event. if If the voltage exceeds the lower boundary value, it is recorded as a voltage over-limit event. if ≤ ≤ If so, the voltage is within a safe range.

[0073] Similarly, for battery temperature parameters, there are situations such as temperature exceeding the limit, temperature exceeding the limit, and temperature within the safe range; for charging current parameters, there are situations such as current exceeding the limit, current exceeding the limit, and current within the safe range.

[0074] Step S4: If the boundary is exceeded, associate at least one abnormal risk mode according to the type of the boundary exceeded, and trigger the reporting of a data packet corresponding to the abnormal risk mode identifier.

[0075] By monitoring and identifying events where each monitored parameter exceeds the upper or lower boundary of the tolerance corridor, the boundary breach type is determined; specifically including: When the terminal voltage parameter exceeds its upper boundary, the associated risk mode is overvoltage risk. When the terminal voltage parameter exceeds its lower boundary, the associated risk mode is undervoltage or connection anomaly. When the battery temperature parameter exceeds its upper limit, the associated risk mode is overheating risk; When the charging current parameter exceeds its upper limit, the associated risk mode is overcurrent risk. When the charging current parameter exceeds its lower boundary, the associated risk modes are charging interruption or communication anomaly. Based on the preset mapping relationship between boundary breach types and abnormal risk patterns, at least one abnormal risk pattern is associated with the corresponding event, and a data packet is generated and reported.

[0076] The charging platform system has a pre-built anomaly mapping knowledge base. This base, constructed based on battery failure mechanism analysis and extensive experimental data, defines the mapping relationship from boundary breach events to abnormal risk modes. When step S3 confirms a boundary breach anomaly, the system executes the following mapping: A voltage breach at the upper limit indicates an overvoltage risk, which can be caused by factors such as abnormally increased battery internal resistance, uncontrolled charger output, BMS voltage sampling failure, and battery balancing failure. A voltage breach at the lower limit indicates an undervoltage or connection abnormality, which can be caused by factors such as abnormally increased battery internal resistance, uncontrolled charger output, BMS voltage sampling failure, and battery balancing failure.

[0077] A breach of the upper temperature limit indicates an overheating risk, which can be caused by factors such as a malfunction in the heat dissipation system, excessively high ambient temperature, excessive charging current, internal short circuit in the battery, or failure of the thermal management system. A breach of the lower temperature limit indicates a temperature sensor malfunction, which can be caused by factors such as a malfunction in the temperature sensor, a disconnected connection, or an abnormal sampling circuit.

[0078] Exceeding the upper current limit poses an overcurrent risk, which can be caused by factors such as charger control loop malfunctions, current sampling deviations, and sudden load changes. Exceeding the lower current limit results in a charging interruption, which can be caused by factors such as communication interruptions, accidental removal of the charging gun, activation of protection devices, and power outages.

[0079] Each risk mode identifier consists of a risk type and a specific parameter name. For overvoltage tendency risks, a contingency plan to reduce charging current can be implemented; for overheat tendency risks, a contingency plan to switch to a lower current stage or suspend charging can be implemented; for connectivity risks, a contingency plan to temporarily interrupt charging and re-establish handshake communication can be implemented. After the contingency plan is implemented, it is monitored whether the key parameters return to the tolerance corridor, and this monitoring result is reported as an indicator of the contingency plan's effectiveness.

[0080] The data packet includes anomaly information, charging context information, and device identification information. The anomaly information includes a risk mode identifier, boundary breach type, and occurrence timestamp; the charging context information includes the current charging stage, current charging current value, and real-time battery SOC; and the device identification information includes the charging station ID and charging pile port number.

[0081] Specifically, the reporting priority of the data packets is adjusted according to the abnormal risk mode; wherein, the reporting priority is divided according to the degree of battery safety risk based on the abnormal risk mode. Voltage abnormalities are assigned the highest reporting priority. According to the set reporting priority, high-priority abnormalities are reported immediately. In the event of a communication interruption, the reported information is stored locally in priority order and resent in priority order after communication is restored.

[0082] It should be noted that when the same type of boundary breach occurs consecutively within the same charging phase, the trend of the breach amplitude and duration is analyzed. If the breach amplitude shows an expanding trend or the duration exceeds the preset proportion of the phase duration, the risk mode is upgraded to an enhanced risk warning indicating a deteriorating trend. If multiple different parameters successively breach the same boundary type within the same charging cycle, a systemic overload risk warning is generated.

[0083] This application calculates the expected parameter trajectories of each parameter based on the optimal current value of the current charging stage using a second-order RC equivalent circuit model, and generates a tolerance corridor centered on this trajectory, rather than relying on fixed numerical thresholds. This fully aligns with the dynamic parameter change patterns of multi-stage constant current charging, reducing the false alarm and false negative rates of anomaly monitoring.

[0084] Meanwhile, the safety risk levels of different monitoring parameters differ fundamentally. This application employs a tolerance corridor design to set upper and lower boundary widths for the differentiated safety characteristics of each parameter. This design enables the tolerance corridor to match the battery's SOC state and temperature environment in real time, maintaining stable monitoring accuracy regardless of whether the battery is new or aged, what charging stage it is in, or the ambient temperature. This solves the problem of poor adaptability of traditional fixed thresholds.

[0085] Furthermore, public charging stations for two-wheeled electric vehicles generally face complex scenarios involving a lack of active cooling, a wide variety of battery brands, and varying degrees of aging. This application eliminates the need for additional cooling equipment, adapting to batteries of different brands and aging levels through a dynamically adjusted tolerance corridor. Simultaneously, both the tolerance design and the dynamic adjustment mechanism are based on the universal characteristics of batteries, requiring no modification to the core charging hardware of the charging station or the use of high-performance hardware for anomaly monitoring based on artificial intelligence. It can be achieved solely through lightweight software algorithm optimization, reducing the cost of technology implementation and possessing strong universality and engineering application value.

[0086] Specifically, in a preferred embodiment of this application, the ambient temperature and humidity of the environment where the charging station is located are obtained, and the boundary of the tolerance corridor is widened or tightened based on the ambient temperature and humidity. The specific implementation process is as follows: The environmental sensors installed at the charging station obtain the ambient temperature value of the environment in real time. With ambient humidity value .

[0087] According to the ambient temperature value Query the preset temperature-compensation coefficient mapping table to determine the temperature compensation coefficient. The mapping table satisfies: when hour, ;when hour, ;when hour, ;in, The preset low temperature threshold, This is the preset high temperature threshold.

[0088] According to the ambient humidity value Query the preset humidity-compensation coefficient mapping table to determine the humidity compensation coefficient. The mapping table satisfies: when hour, ;when hour, ;when hour, ;in, The preset low humidity threshold, This is the preset high humidity threshold.

[0089] Calculate the comprehensive compensation coefficient: The upper boundary width value of the tolerance corridor and lower boundary width value Multiply by the aforementioned comprehensive compensation coefficient respectively The compensated boundary width value is obtained as follows: With expected parameter trajectory Centered on the boundary width value, use the compensated boundary width value. and The generated tolerance corridor boundary is represented as follows: In this embodiment, the voltage boundary is appropriately relaxed in low-temperature environments and the temperature boundary is appropriately tightened in high-temperature environments. The charging current boundary is compensated based on ambient humidity, and the current boundary is appropriately tightened in high-humidity environments to reduce the risk of condensation. By introducing an ambient temperature and humidity compensation mechanism, charging safety monitoring can sense and respond to changes in external climate conditions. This avoids false alarms or missed alarms that are prone to occur in extreme weather conditions, improves the adaptability of public charging stations to all-weather operation in complex outdoor environments, and reduces the risk of battery thermal runaway caused by environmental factors.

[0090] Please see Figure 3 This application also provides an automatic reporting system for charging station charging anomalies, used in the aforementioned method, including: The data acquisition unit is used to acquire the real-time operating parameters of the battery during the multi-stage constant current charging process; A safety boundary generation unit is used to determine the current state of the battery based on the real-time operating parameters, and generate a tolerance corridor as a safety benchmark based on the current state of the battery. The tolerance corridor defines upper and lower boundaries centered on the expected parameter trajectory, allowing real-time parameter fluctuations, and the width of the upper and lower boundaries is set to be different for each other according to the parameter type and charging stage. The judgment unit is used to determine whether the monitoring parameters of the real-time operating parameters exceed the upper and lower boundaries of the tolerance corridor based on the monitoring parameters of the real-time operating parameters. An anomaly reporting unit is used to, if a boundary is exceeded, associate at least one anomaly risk mode with the type of boundary exceeded, and trigger the reporting of a data packet corresponding to the anomaly risk mode identifier.

[0091] The functional explanation of each unit in this embodiment is the same as that of a method for automatically reporting charging abnormalities in a charging station, and the technical effect is the same, so it will not be repeated here.

[0092] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0093] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0094] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the specification of the present invention.

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.

2. The method according to claim 1, characterized in that, The generation 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 parameter; the monitored parameters at least include the terminal voltage parameter, the battery temperature parameter, and the 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.

3. The method of claim 2, wherein the method further comprises: It also 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.

4. The method of claim 2, wherein the method further comprises: The determination of the initial width values of the upper and lower boundaries of each monitored parameter according to the safety characteristics of the monitored parameter comprises: for the terminal 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.

5. The method of claim 4, 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.

6. The method according to claim 5, wherein, 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.

7. The method according to claim 6, characterized in that, Further comprising: Multiplying the initial width value by the first adjustment coefficient and the second adjustment coefficient to obtain an adjusted width value.

8. 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, and a data packet is generated and reported.

9. The method of claim 8, wherein the method further comprises: 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.

10. A charging station charging anomaly automatic reporting system for implementing the method of any one of claims 1-9, 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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