Battery internal resistance online monitoring and early warning method and system based on charging pile

Abstract

The invention provides a battery internal resistance online monitoring and early warning method and system based on a charging pile, and belongs to the technical field of electric vehicle battery state monitoring. The method comprises the steps that S1, the charging pile outputs power; s2, collecting charging data, and starting an internal resistance monitoring operation when a charging working condition meets an internal resistance monitoring condition; s3, superposing step current on the basis of the output current to obtain test current, and controlling the charging gun to output power for a preset duration; s4, acquiring corresponding battery end voltages, calculating the internal resistance of the battery based on the battery end voltages and the test current, and uploading the data to the server; s5, the server constructs and stores a battery health file; and S6, performing transverse and longitudinal comparison based on the health archives of the batteries to generate a fault early warning notice. The battery internal resistance monitoring and early warning system has the advantages that normalized, trended and accurate monitoring and early warning of the battery internal resistance are realized, and the use safety of the electric vehicle is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle battery state monitoring technology, and in particular to a method and system for online monitoring and early warning of battery internal resistance based on charging piles. Background Technology

[0002] The power battery is the core component of an electric vehicle, and its health directly determines the vehicle's driving range, safety performance, and resale value. Battery internal resistance is a key parameter characterizing battery health; its changes can sensitively reflect internal aging, degradation, and even micro-short circuits. Increased battery internal resistance leads to reduced charging and discharging efficiency, increased heat generation, accelerated capacity decay, and in severe cases, may cause thermal runaway and other safety accidents. Therefore, accurate and convenient monitoring and evaluation of battery internal resistance is crucial for ensuring vehicle safety, extending battery life, and improving user experience.

[0003] Currently, the detection of the internal resistance of automotive power batteries mainly relies on the following methods: 1. Offline Testing Method: This method requires removing the power battery from the electric vehicle or having professionals measure it in a specific location (such as a repair shop) using specialized equipment (such as an internal resistance tester or an electrochemical workstation). The disadvantages of this method are obvious: the testing process is cumbersome, costly, and cannot be integrated into users' daily driving scenarios, making routine monitoring difficult. Essentially, it is a "post-inspection" method, unable to track dynamic changes in battery status, let alone provide early fault warnings.

[0004] 2. Estimation Methods Based on Battery Management Systems (BMS): Existing electric vehicles are typically equipped with battery management systems, which can estimate the battery's internal resistance or state of health by monitoring parameters such as voltage and current, and combining algorithms such as the ampere-hour integral method and Kalman filtering. However, such methods are limited by the computing power of the onboard BMS, the accuracy of sensors, and the complex and variable operating conditions of the vehicle, resulting in poor accuracy and consistency of the estimation results. More importantly, BMS mainly relies on the vehicle's own instantaneous data for judgment, lacking comparison with massive amounts of external data and systematic tracking and analysis of its own long-term historical data, leading to relatively one-sided evaluation results and insufficient accuracy and foresight in early warning.

[0005] 3. Simple Monitoring in Charging Scenarios: With the widespread adoption of charging stations, some existing technologies have emerged that utilize the charging process for battery status monitoring. However, these solutions are relatively limited in function, typically only capable of single, isolated measurements. For example, they might acquire a set of voltage and current data at the start or end of charging to calculate an instantaneous internal resistance value. This "single-point snapshot" measurement cannot reflect the dynamic changes in internal resistance as the battery ages and the number of charging cycles. Their judgment criteria are mostly based on static, universal thresholds, failing to consider individual differences arising from different vehicles and user habits, easily leading to misjudgments or missed judgments, and severely delayed warning information.

[0006] In summary, existing technologies have the following core defects: 1. Inability to be routine: Offline detection methods interrupt user use and are difficult to perform frequently; 2. Lack of trend analysis: Existing online methods are mostly single-point measurements, which cannot form a continuous historical data chain, making it difficult to reveal the changing patterns of internal resistance and achieve trend-based predictive maintenance; 3. Data isolation and one-sided evaluation: Judgment is limited to horizontal comparisons or static models, lacking continuous tracking and personalized analysis of individual battery lifecycle data, resulting in insufficient accuracy and foresight in early warning.

[0007] Therefore, how to provide a method and system for online monitoring and early warning of battery internal resistance based on charging piles, so as to realize normalized, trend-based and accurate forward-looking monitoring and early warning of battery internal resistance, and thus improve the safety of electric vehicle use, has become an urgent technical problem to be solved. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method and system for online monitoring and early warning of battery internal resistance based on charging piles, so as to realize normalized, trend-based and accurate forward-looking monitoring and early warning of battery internal resistance, thereby improving the safety of electric vehicle use.

[0009] In a first aspect, the present invention provides a method for online monitoring and early warning of battery internal resistance based on a charging pile, comprising the following steps: Step S1: The charging pile is pre-set with an internal resistance monitoring condition, obtains the VIN code and vehicle information of the electric vehicle, and outputs power based on the output current carried by the received charging command to perform a charging operation on the power battery of the electric vehicle. Step S2: The charging pile collects charging data in real time based on the received monitoring instructions. When the charging conditions meet the internal resistance monitoring conditions, the internal resistance monitoring operation is started. Step S3: The charging pile adds a preset stepped current to the output current to obtain the test current, and controls the charging gun to output power based on the test current for a preset duration. Step S4: Within the preset time period, when the charging current is stable, the corresponding battery terminal voltage is collected through the BMS, the corresponding battery internal resistance is calculated based on the battery terminal voltage and the test current, and the battery internal resistance, battery terminal voltage, test current, charging data and vehicle information are bound with the VIN code and uploaded to the server. Step S5: The server constructs and stores a battery health profile based on the received battery internal resistance, battery terminal voltage, test current, charging data, vehicle information, and VIN code. Step S6: The server performs horizontal and vertical comparisons of the battery internal resistance based on the health records of each battery, and generates a fault warning notification based on the comparison data. Step S7: The server generates a battery internal resistance monitoring report based on the battery health record and fault warning notification, and pushes the battery internal resistance monitoring report to the electric vehicle or mobile terminal corresponding to the VIN code.

[0010] Furthermore, step S1 specifically includes: The charging pile is pre-set with internal resistance monitoring conditions, which include at least the SOC range, charging temperature range, and power margin percentage. After establishing a connection with the electric vehicle via the charging gun, it communicates with the electric vehicle's BMS to obtain the electric vehicle's VIN code and vehicle information; the vehicle information includes at least the vehicle model and battery model. The charging station outputs power based on the output current carried by the received charging command to perform a charging operation on the electric vehicle's power battery.

[0011] Furthermore, step S2 specifically includes: The charging pile collects charging data in real time, including charging voltage, charging current, charging temperature, SOC, and charging timestamp, based on monitoring instructions sent by the server or electric vehicle. It also monitors the current charging conditions and automatically starts the internal resistance monitoring operation when the charging conditions meet the internal resistance monitoring conditions.

[0012] Furthermore, step S4 specifically includes: Within the preset time period corresponding to each of the aforementioned step currents, when the charging current is stable, the battery terminal voltage corresponding to the power battery is collected by listening to the BMS message of the BMS, and a UI curve is plotted based on each of the aforementioned battery terminal voltages and the test current. The corresponding battery internal resistance is calculated based on the slope of the UI curve. After binding the battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information with the VIN code, the data is encrypted and uploaded to the server.

[0013] Furthermore, step S6 specifically includes: Based on the battery health records, the server performs horizontal comparisons with electric vehicles of the same vehicle model, battery model, charging temperature, and SOC to obtain horizontal comparison data; it also performs vertical comparisons with the battery health records and the historical data of the electric vehicle to obtain vertical comparison data; and, by combining time series analysis or machine learning algorithms, it predicts the internal resistance change trend based on the vertical comparison data, and generates a fault warning notification based on the horizontal comparison data, the vertical comparison data, and the internal resistance change trend.

[0014] Secondly, the present invention provides a battery internal resistance online monitoring and early warning system based on a charging pile, comprising the following modules: The charging module is used to pre-set an internal resistance monitoring condition for the charging pile, obtain the VIN code and vehicle information of the electric vehicle, and output power based on the output current carried by the received charging command to perform charging operation on the power battery of the electric vehicle. The charging data acquisition module is used to collect charging data in real time based on the received monitoring instructions. When the charging conditions meet the internal resistance monitoring conditions, the internal resistance monitoring operation is started. The stepped current output module is used to add a preset stepped current to the output current to obtain a test current, and control the charging gun to output power based on the test current for a preset duration. The battery internal resistance calculation module is used to collect the corresponding battery terminal voltage through BMS within the preset time period when the charging current is stable, calculate the corresponding battery internal resistance based on each battery terminal voltage and the test current, and upload the battery internal resistance, battery terminal voltage, test current, charging data and vehicle information to the server after binding them with the VIN code. The battery health record construction module is used by the server to construct and store the battery health record based on the received battery internal resistance, battery terminal voltage, test current, charging data, vehicle information and VIN code. The horizontal and vertical comparison module is used by the server to perform horizontal and vertical comparisons of the internal resistance of the batteries based on the health records of each battery, and generate fault warning notifications based on the comparison data. The monitoring report push module is used by the server to generate a battery internal resistance monitoring report based on the battery health record and fault warning notification, and push the battery internal resistance monitoring report to the electric vehicle or mobile terminal corresponding to the VIN code.

[0015] Furthermore, the charging module is specifically used for: The charging pile is pre-set with internal resistance monitoring conditions, which include at least the SOC range, charging temperature range, and power margin percentage. After establishing a connection with the electric vehicle via the charging gun, it communicates with the electric vehicle's BMS to obtain the electric vehicle's VIN code and vehicle information; the vehicle information includes at least the vehicle model and battery model. The charging station outputs power based on the output current carried by the received charging command to perform a charging operation on the electric vehicle's power battery.

[0016] Furthermore, the charging data acquisition module is specifically used for: The charging pile collects charging data in real time, including charging voltage, charging current, charging temperature, SOC, and charging timestamp, based on monitoring instructions sent by the server or electric vehicle. It also monitors the current charging conditions and automatically starts the internal resistance monitoring operation when the charging conditions meet the internal resistance monitoring conditions.

[0017] Furthermore, the battery internal resistance calculation module is specifically used for: Within the preset time period corresponding to each of the aforementioned step currents, when the charging current is stable, the battery terminal voltage corresponding to the power battery is collected by listening to the BMS message of the BMS, and a UI curve is plotted based on each of the aforementioned battery terminal voltages and the test current. The corresponding battery internal resistance is calculated based on the slope of the UI curve. After binding the battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information with the VIN code, the data is encrypted and uploaded to the server.

[0018] Furthermore, the horizontal and vertical comparison module is specifically used for: Based on the battery health records, the server performs horizontal comparisons with electric vehicles of the same vehicle model, battery model, charging temperature, and SOC to obtain horizontal comparison data; it also performs vertical comparisons with the battery health records and the historical data of the electric vehicle to obtain vertical comparison data; and, by combining time series analysis or machine learning algorithms, it predicts the internal resistance change trend based on the vertical comparison data, and generates a fault warning notification based on the horizontal comparison data, the vertical comparison data, and the internal resistance change trend.

[0019] The advantages of this invention are: 1. By pre-setting internal resistance monitoring conditions on the charging pile, the VIN code and vehicle information of the electric vehicle are obtained. Power output is performed based on the output current carried by the charging command to charge the power battery of the electric vehicle. The charging pile collects charging data in real time based on the monitoring command. When the charging condition meets the internal resistance monitoring conditions, the internal resistance monitoring operation is initiated. A preset stepped current is superimposed on the output current to obtain the test current. The charging gun is controlled to output power based on the test current for a preset duration. Within the preset duration, when the charging current is stable, the corresponding battery terminal voltage is collected through the BMS. The corresponding battery internal resistance is calculated based on the battery terminal voltage and the test current. The battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information are bound to the VIN code and uploaded to the server. The server constructs and stores battery health records based on the battery internal resistance, battery terminal voltage, test current, charging data, vehicle information, and VIN code. Horizontal and vertical comparisons of battery internal resistance are performed based on the battery health records. Fault warning notifications are generated based on the comparison data. Battery health records and fault warning notifications generate battery internal resistance monitoring reports, which are then pushed to the electric vehicle or mobile terminal corresponding to the VIN code. This involves automatically performing internal resistance monitoring (routinely) during daily charging at charging stations, binding the battery internal resistance data obtained from each test to the VIN code and uploading it to the server to build a continuously updated battery health record for each vehicle. The server uses this battery health record data to establish benchmarks through horizontal comparisons between vehicles of the same model and to track historical data for the same vehicle to capture trends. Finally, time series or machine learning algorithms are used to predict internal resistance trends, upgrading from static threshold-based judgments to precise, forward-looking warnings based on individualized dynamic trends. This transforms charging stations into distributed monitoring terminals, enabling early and accurate risk identification of battery health status through intelligent cloud data analysis. Ultimately, this achieves routine, trend-based, and precise forward-looking monitoring and warning of battery internal resistance, significantly improving the safety of electric vehicle use.

[0020] 2. By automatically initiating internal resistance monitoring during the charging process at the charging station, battery data can be collected in real time without additional equipment or interruption of the charging process, significantly improving monitoring efficiency and avoiding the cumbersome steps of traditional offline testing. At the same time, by utilizing the existing infrastructure of the charging station, the implementation cost is reduced, allowing battery health checks to be seamlessly integrated into daily charging scenarios, providing great convenience for users.

[0021] 3. The test current is obtained by superimposing a preset stepped current on the output current, and the battery internal resistance is calculated based on this. This method only makes fine adjustments based on the charging process and will not significantly interfere with the normal charging of the battery. This non-invasive design avoids battery damage or charging interruption that may occur in traditional testing, ensuring the safety and reliability of the test, while improving the accuracy of the data, because the test is carried out under actual charging conditions.

[0022] 4. After collecting data such as battery internal resistance and voltage and binding them with the VIN code, the data is uploaded to the server for horizontal and vertical comparison. Combined with time series analysis or machine learning algorithms, the internal resistance change trend is predicted. This intelligent analysis can detect battery degradation or failure risks at an early stage and generate early warning notifications, thereby helping users or operators to take timely maintenance measures, extend battery life, and improve the overall safety and reliability of electric vehicles.

[0023] 5. The monitoring operation is automatically triggered by preset internal resistance monitoring conditions (such as SOC range and temperature range), and the data is processed uniformly by the server, supporting the comparison of multiple vehicle and battery models. This automated design reduces manual intervention and lowers operating costs. At the same time, through standardized processes and VIN code binding, the method is easy to extend to different charging pile networks and electric vehicle models, with good versatility and scalability, which is suitable for the future development needs of smart grids and vehicle networks.

[0024] 6. By generating battery internal resistance monitoring reports and pushing them to users' electric vehicles or mobile terminals, not only is the transparency and availability of data improved, but users are also encouraged to actively participate in battery maintenance. The battery health profile built by the server supports long-term tracking and analysis, providing data support for battery recycling, warranty, or optimized charging strategies, thereby achieving refined management of the entire battery life cycle and enhancing the added value and market competitiveness of the product.

[0025] 7. The charging pile automatically monitors the battery's internal resistance online during the charging process, adopts a non-invasive testing method to improve efficiency and reduce interference with the battery, and integrates intelligent data analysis functions to achieve horizontal and vertical comparisons and trend predictions, thereby enhancing battery safety management and early warning capabilities; its high degree of automation and scalability are suitable for large-scale applications, and it enhances user participation through data binding and report push, promoting refined management of the entire battery life cycle. Attached Figure Description

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0027] Figure 1 This is a flowchart of a battery internal resistance online monitoring and early warning method based on a charging pile according to the present invention.

[0028] Figure 2 This is a schematic diagram of the structure of an online monitoring and early warning system for battery internal resistance based on a charging pile according to the present invention. Detailed Implementation

[0029] The technical solution in this application embodiment follows the general idea as follows: Internal resistance monitoring is automatically performed during daily charging at the charging station. Data such as battery internal resistance obtained from each test is bound to the VIN code and uploaded to the server, creating a continuously updated battery health profile for each vehicle. The server uses this battery health profile data to perform horizontal comparisons between vehicles of the same model to establish benchmarks, and vertical tracking of historical data for the same vehicle to capture changing trends. Finally, time series or machine learning algorithms are combined to predict internal resistance change trends, thereby upgrading from static threshold-based judgments to accurate forward-looking warnings based on individualized dynamic trends. In other words, the charging station is transformed into a distributed detection terminal. Through intelligent cloud data analysis, early and accurate risk identification of battery health status is achieved, ultimately realizing normalized, trend-based, and accurate forward-looking monitoring and warning of battery internal resistance, thereby improving the safety of electric vehicle use.

[0030] Please refer to Figures 1 to 2 As shown, a preferred embodiment of the present invention, a method for online monitoring and early warning of battery internal resistance based on charging piles, includes the following steps: Step S1: The charging pile is pre-set with an internal resistance monitoring condition, obtains the VIN code and vehicle information of the electric vehicle, and outputs power based on the output current carried by the received charging command to perform a charging operation on the power battery of the electric vehicle. Step S2: The charging pile collects charging data in real time based on the received monitoring instructions. When the charging conditions meet the internal resistance monitoring conditions, the internal resistance monitoring operation is started. Step S3: The charging pile adds a preset stepped current to the output current to obtain the test current, and controls the charging gun to output power based on the test current for a preset duration. For example, if the output current is 5A, the step current is 0.2A, and the preset duration is 10 seconds, then the charging pile will output power at 5.2A for 10 seconds, and then return to 5A for power output. Step S4: Within the preset time period, when the charging current is stable, the corresponding battery terminal voltage is collected through the BMS, the corresponding battery internal resistance is calculated based on the battery terminal voltage and the test current, and the battery internal resistance, battery terminal voltage, test current, charging data and vehicle information are bound with the VIN code and uploaded to the server. Step S5: The server constructs and stores a battery health profile based on the received battery internal resistance, battery terminal voltage, test current, charging data, vehicle information, and VIN code. Step S6: The server performs horizontal and vertical comparisons of the battery internal resistance based on the health records of each battery, and generates a fault warning notification based on the comparison data. Step S7: The server generates a battery internal resistance monitoring report based on the battery health record and fault warning notification, and pushes the battery internal resistance monitoring report to the electric vehicle or mobile terminal corresponding to the VIN code. The battery internal resistance monitoring report includes at least the current battery internal resistance, the battery internal resistance position within a reasonable range, historical change curves, predicted trend lines, health score, and maintenance suggestions. By pushing the battery internal resistance monitoring report to the electric vehicle or mobile terminal, users can understand the entire life cycle of their battery's internal resistance.

[0031] Step S1 specifically involves: The charging pile has a pre-set internal resistance monitoring condition, which includes at least the SOC range, charging temperature range, and power margin percentage. For example, the internal resistance monitoring condition is set as follows: SOC is between 20% and 80%, charging temperature is between 15°C and 35°C, and power margin percentage is 20%. Setting the power margin percentage is to ensure that the battery internal resistance detection can be performed safely, accurately, and imperceptibly. Internal resistance detection requires the charging pile to apply a changing current excitation (such as stepped current or pulse). This changing current is a "dynamic load" added on top of the basic output current. If the charging pile does not have a power margin, it may have to reduce the basic output current in order to execute the command in order to make room for the current excitation.

[0032] After establishing a connection with the electric vehicle via the charging gun, it communicates with the electric vehicle's BMS to obtain the electric vehicle's VIN code and vehicle information; the vehicle information includes at least the vehicle model and battery model. The charging station outputs power based on the output current carried by the received charging command to perform a charging operation on the electric vehicle's power battery.

[0033] Step S2 specifically involves: The charging pile collects charging data in real time, including charging voltage, charging current, charging temperature, SOC, and charging timestamp, based on monitoring instructions sent by the server or electric vehicle. It also monitors the current charging conditions and automatically starts the internal resistance monitoring operation when the charging conditions meet the internal resistance monitoring conditions.

[0034] Step S4 specifically involves: Within the preset time period corresponding to each of the aforementioned step currents, when the charging current is stable, the battery terminal voltage corresponding to the power battery is collected by listening to the BMS message of the BMS, and a UI curve is plotted based on each of the aforementioned battery terminal voltages and the test current. The corresponding battery internal resistance is calculated based on the slope of the UI curve. After binding the battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information with the VIN code, the data is encrypted and uploaded to the server.

[0035] This involves collecting multiple sets of (I, U) data, calculating the battery's internal resistance based on Ohm's law (R=ΔU / ΔI), and using a linear fitting method when there are more than two data points. The test current I is used as the abscissa and the battery terminal voltage U is used as the ordinate to fit a UI curve. The slope of the UI curve is the DC internal resistance value of the battery in the current state (battery internal resistance). Finally, the multiple sets of (I, U) data and the calculated battery internal resistance are uploaded to the server.

[0036] Step S6 specifically involves: Based on the battery health records, the server performs horizontal comparisons with electric vehicles of the same model, battery type, charging temperature, and SOC to obtain horizontal comparison data. It also performs vertical comparisons between the battery health records and historical data of the electric vehicle to obtain vertical comparison data. Combining time series analysis (such as the ARIMA model) or machine learning algorithms, the server predicts the internal resistance change trend (predicting the battery internal resistance after a certain period or number of cycles) based on the vertical comparison data. Finally, it generates fault warning notifications based on the horizontal comparison data, the vertical comparison data, and the internal resistance change trend. Through vertical comparison, the server can analyze the trend and growth rate of battery internal resistance over time and cycle count.

[0037] In practice, if it is found that the current battery internal resistance exceeds the upper limit of the reasonable range of the group, the short-term growth rate of the battery internal resistance exceeds the threshold, or the predicted value will exceed the danger threshold, it will be judged as abnormal and different levels of fault warning notifications (prompt, warning, danger) will be generated.

[0038] A preferred embodiment of the battery internal resistance online monitoring and early warning system based on charging piles of the present invention includes the following modules: The charging module is used to pre-set an internal resistance monitoring condition for the charging pile, obtain the VIN code and vehicle information of the electric vehicle, and output power based on the output current carried by the received charging command to perform charging operation on the power battery of the electric vehicle. The charging data acquisition module is used to collect charging data in real time based on the received monitoring instructions. When the charging conditions meet the internal resistance monitoring conditions, the internal resistance monitoring operation is started. The stepped current output module is used to add a preset stepped current to the output current to obtain a test current, and control the charging gun to output power based on the test current for a preset duration. For example, if the output current is 5A, the step current is 0.2A, and the preset duration is 10 seconds, then the charging pile will output power at 5.2A for 10 seconds, and then return to 5A for power output. The battery internal resistance calculation module is used to collect the corresponding battery terminal voltage through BMS within the preset time period when the charging current is stable, calculate the corresponding battery internal resistance based on each battery terminal voltage and the test current, and upload the battery internal resistance, battery terminal voltage, test current, charging data and vehicle information to the server after binding them with the VIN code. The battery health record construction module is used by the server to construct and store the battery health record based on the received battery internal resistance, battery terminal voltage, test current, charging data, vehicle information and VIN code. The horizontal and vertical comparison module is used by the server to perform horizontal and vertical comparisons of the internal resistance of the batteries based on the health records of each battery, and generate fault warning notifications based on the comparison data. The monitoring report push module is used by the server to generate a battery internal resistance monitoring report based on the battery health record and fault warning notification, and push the battery internal resistance monitoring report to the electric vehicle or mobile terminal corresponding to the VIN code.

[0039] The battery internal resistance monitoring report includes at least the current battery internal resistance, its position within a reasonable range, historical variation curves, a predicted trend line, a health score, and maintenance recommendations. By pushing the battery internal resistance monitoring report to electric vehicles or mobile terminals, users can gain a complete understanding of their battery's internal resistance throughout its entire lifecycle.

[0040] The charging module is specifically used for: The charging pile has a pre-set internal resistance monitoring condition, which includes at least the SOC range, charging temperature range, and power margin percentage. For example, the internal resistance monitoring condition is set as follows: SOC is between 20% and 80%, charging temperature is between 15°C and 35°C, and power margin percentage is 20%. Setting the power margin percentage is to ensure that the battery internal resistance detection can be performed safely, accurately, and imperceptibly. Internal resistance detection requires the charging pile to apply a changing current excitation (such as stepped current or pulse). This changing current is a "dynamic load" added on top of the basic output current. If the charging pile does not have a power margin, it may have to reduce the basic output current in order to execute the command in order to make room for the current excitation.

[0041] After establishing a connection with the electric vehicle via the charging gun, it communicates with the electric vehicle's BMS to obtain the electric vehicle's VIN code and vehicle information; the vehicle information includes at least the vehicle model and battery model. The charging station outputs power based on the output current carried by the received charging command to perform a charging operation on the electric vehicle's power battery.

[0042] The charging data acquisition module is specifically used for: The charging pile collects charging data in real time, including charging voltage, charging current, charging temperature, SOC, and charging timestamp, based on monitoring instructions sent by the server or electric vehicle. It also monitors the current charging conditions and automatically starts the internal resistance monitoring operation when the charging conditions meet the internal resistance monitoring conditions.

[0043] The battery internal resistance calculation module is specifically used for: Within the preset time period corresponding to each of the aforementioned step currents, when the charging current is stable, the battery terminal voltage corresponding to the power battery is collected by listening to the BMS message of the BMS, and a UI curve is plotted based on each of the aforementioned battery terminal voltages and the test current. The corresponding battery internal resistance is calculated based on the slope of the UI curve. After binding the battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information with the VIN code, the data is encrypted and uploaded to the server.

[0044] This involves collecting multiple sets of (I, U) data, calculating the battery's internal resistance based on Ohm's law (R=ΔU / ΔI), and using a linear fitting method when there are more than two data points. The test current I is used as the abscissa and the battery terminal voltage U is used as the ordinate to fit a UI curve. The slope of the UI curve is the DC internal resistance value of the battery in the current state (battery internal resistance). Finally, the multiple sets of (I, U) data and the calculated battery internal resistance are uploaded to the server.

[0045] The horizontal and vertical comparison module is specifically used for: Based on the battery health records, the server performs horizontal comparisons with electric vehicles of the same model, battery type, charging temperature, and SOC to obtain horizontal comparison data. It also performs vertical comparisons between the battery health records and historical data of the electric vehicle to obtain vertical comparison data. Combining time series analysis (such as the ARIMA model) or machine learning algorithms, the server predicts the internal resistance change trend (predicting the battery internal resistance after a certain period or number of cycles) based on the vertical comparison data. Finally, it generates fault warning notifications based on the horizontal comparison data, the vertical comparison data, and the internal resistance change trend. Through vertical comparison, the server can analyze the trend and growth rate of battery internal resistance over time and cycle count.

[0046] In practice, if it is found that the current battery internal resistance exceeds the upper limit of the reasonable range of the group, the short-term growth rate of the battery internal resistance exceeds the threshold, or the predicted value will exceed the danger threshold, it will be judged as abnormal and different levels of fault warning notifications (prompt, warning, danger) will be generated.

[0047] In summary, the advantages of this invention are as follows: 1. By pre-setting internal resistance monitoring conditions on the charging pile, the VIN code and vehicle information of the electric vehicle are obtained. Power output is performed based on the output current carried by the charging command to charge the power battery of the electric vehicle. The charging pile collects charging data in real time based on the monitoring command. When the charging condition meets the internal resistance monitoring conditions, the internal resistance monitoring operation is initiated. A preset stepped current is superimposed on the output current to obtain the test current. The charging gun is controlled to output power based on the test current for a preset duration. Within the preset duration, when the charging current is stable, the corresponding battery terminal voltage is collected through the BMS. The corresponding battery internal resistance is calculated based on the battery terminal voltage and the test current. The battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information are bound to the VIN code and uploaded to the server. The server constructs and stores battery health records based on the battery internal resistance, battery terminal voltage, test current, charging data, vehicle information, and VIN code. Horizontal and vertical comparisons of battery internal resistance are performed based on the battery health records. Fault warning notifications are generated based on the comparison data. Battery health records and fault warning notifications generate battery internal resistance monitoring reports, which are then pushed to the electric vehicle or mobile terminal corresponding to the VIN code. This involves automatically performing internal resistance monitoring (routinely) during daily charging at charging stations, binding the battery internal resistance data obtained from each test to the VIN code and uploading it to the server to build a continuously updated battery health record for each vehicle. The server uses this battery health record data to establish benchmarks through horizontal comparisons between vehicles of the same model and to track historical data for the same vehicle to capture trends. Finally, time series or machine learning algorithms are used to predict internal resistance trends, upgrading from static threshold-based judgments to precise, forward-looking warnings based on individualized dynamic trends. This transforms charging stations into distributed monitoring terminals, enabling early and accurate risk identification of battery health status through intelligent cloud data analysis. Ultimately, this achieves routine, trend-based, and precise forward-looking monitoring and warning of battery internal resistance, significantly improving the safety of electric vehicle use.

[0048] 2. By automatically initiating internal resistance monitoring during the charging process at the charging station, battery data can be collected in real time without additional equipment or interruption of the charging process, significantly improving monitoring efficiency and avoiding the cumbersome steps of traditional offline testing. At the same time, by utilizing the existing infrastructure of the charging station, the implementation cost is reduced, allowing battery health checks to be seamlessly integrated into daily charging scenarios, providing great convenience for users.

[0049] 3. The test current is obtained by superimposing a preset stepped current on the output current, and the battery internal resistance is calculated based on this. This method only makes fine adjustments based on the charging process and will not significantly interfere with the normal charging of the battery. This non-invasive design avoids battery damage or charging interruption that may occur in traditional testing, ensuring the safety and reliability of the test, while improving the accuracy of the data, because the test is carried out under actual charging conditions.

[0050] 4. After collecting data such as battery internal resistance and voltage and binding them with the VIN code, the data is uploaded to the server for horizontal and vertical comparison. Combined with time series analysis or machine learning algorithms, the internal resistance change trend is predicted. This intelligent analysis can detect battery degradation or failure risks at an early stage and generate early warning notifications, thereby helping users or operators to take timely maintenance measures, extend battery life, and improve the overall safety and reliability of electric vehicles.

[0051] 5. The monitoring operation is automatically triggered by preset internal resistance monitoring conditions (such as SOC range and temperature range), and the data is processed uniformly by the server, supporting the comparison of multiple vehicle and battery models. This automated design reduces manual intervention and lowers operating costs. At the same time, through standardized processes and VIN code binding, the method is easy to extend to different charging pile networks and electric vehicle models, with good versatility and scalability, which is suitable for the future development needs of smart grids and vehicle networks.

[0052] 6. By generating battery internal resistance monitoring reports and pushing them to users' electric vehicles or mobile terminals, not only is the transparency and availability of data improved, but users are also encouraged to actively participate in battery maintenance. The battery health profile built by the server supports long-term tracking and analysis, providing data support for battery recycling, warranty, or optimized charging strategies, thereby achieving refined management of the entire battery life cycle and enhancing the added value and market competitiveness of the product.

[0053] 7. The charging pile automatically monitors the battery's internal resistance online during the charging process, adopts a non-invasive testing method to improve efficiency and reduce interference with the battery, and integrates intelligent data analysis functions to achieve horizontal and vertical comparisons and trend predictions, thereby enhancing battery safety management and early warning capabilities; its high degree of automation and scalability are suitable for large-scale applications, and it enhances user participation through data binding and report push, promoting refined management of the entire battery life cycle.

[0054] While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the present invention. Equivalent modifications and variations made by those skilled in the art in accordance with the spirit of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for online monitoring and early warning of battery internal resistance based on charging piles, characterized in that: Includes the following steps: Step S1: The charging pile is pre-set with an internal resistance monitoring condition, obtains the VIN code and vehicle information of the electric vehicle, and outputs power based on the output current carried by the received charging command to perform a charging operation on the power battery of the electric vehicle. Step S2: The charging pile collects charging data in real time based on the received monitoring instructions. When the charging conditions meet the internal resistance monitoring conditions, the internal resistance monitoring operation is started. Step S3: The charging pile adds a preset stepped current to the output current to obtain the test current, and controls the charging gun to output power based on the test current for a preset duration. Step S4: Within the preset time period, when the charging current is stable, the corresponding battery terminal voltage is collected through the BMS, the corresponding battery internal resistance is calculated based on the battery terminal voltage and the test current, and the battery internal resistance, battery terminal voltage, test current, charging data and vehicle information are bound with the VIN code and uploaded to the server. Step S5: The server constructs and stores a battery health profile based on the received battery internal resistance, battery terminal voltage, test current, charging data, vehicle information, and VIN code. Step S6: The server performs horizontal and vertical comparisons of the battery internal resistance based on the health records of each battery, and generates a fault warning notification based on the comparison data. Step S7: The server generates a battery internal resistance monitoring report based on the battery health record and fault warning notification, and pushes the battery internal resistance monitoring report to the electric vehicle or mobile terminal corresponding to the VIN code.

2. The method for online monitoring and early warning of battery internal resistance based on charging piles as described in claim 1, characterized in that: Step S1 specifically involves: The charging pile is pre-set with internal resistance monitoring conditions, which include at least the SOC range, charging temperature range, and power margin percentage. After establishing a connection with the electric vehicle via the charging gun, it communicates with the electric vehicle's BMS to obtain the electric vehicle's VIN code and vehicle information; the vehicle information includes at least the vehicle model and battery model. The charging station outputs power based on the output current carried by the received charging command to perform a charging operation on the electric vehicle's power battery.

3. The method for online monitoring and early warning of battery internal resistance based on charging piles as described in claim 1, characterized in that: Step S2 specifically involves: The charging pile collects charging data in real time, including charging voltage, charging current, charging temperature, SOC, and charging timestamp, based on monitoring instructions sent by the server or electric vehicle. It also monitors the current charging conditions and automatically starts the internal resistance monitoring operation when the charging conditions meet the internal resistance monitoring conditions.

4. The method for online monitoring and early warning of battery internal resistance based on charging piles as described in claim 1, characterized in that: Step S4 specifically involves: Within the preset time period corresponding to each of the aforementioned step currents, when the charging current is stable, the battery terminal voltage corresponding to the power battery is collected by listening to the BMS message of the BMS, and a UI curve is plotted based on each of the aforementioned battery terminal voltages and the test current. The corresponding battery internal resistance is calculated based on the slope of the UI curve. After binding the battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information with the VIN code, the data is encrypted and uploaded to the server.

5. The method for online monitoring and early warning of battery internal resistance based on charging piles as described in claim 1, characterized in that: Step S6 specifically involves: Based on the battery health records, the server performs horizontal comparisons with electric vehicles of the same vehicle model, battery model, charging temperature, and SOC to obtain horizontal comparison data; it also performs vertical comparisons with the battery health records and the historical data of the electric vehicle to obtain vertical comparison data; and, by combining time series analysis or machine learning algorithms, it predicts the internal resistance change trend based on the vertical comparison data, and generates a fault warning notification based on the horizontal comparison data, the vertical comparison data, and the internal resistance change trend.

6. A battery internal resistance online monitoring and early warning system based on charging piles, characterized in that: Includes the following modules: The charging module is used to pre-set an internal resistance monitoring condition for the charging pile, obtain the VIN code and vehicle information of the electric vehicle, and output power based on the output current carried by the received charging command to perform charging operation on the power battery of the electric vehicle. The charging data acquisition module is used to collect charging data in real time based on the received monitoring instructions. When the charging conditions meet the internal resistance monitoring conditions, the internal resistance monitoring operation is started. The stepped current output module is used to add a preset stepped current to the output current to obtain a test current, and control the charging gun to output power based on the test current for a preset duration. The battery internal resistance calculation module is used to collect the corresponding battery terminal voltage through BMS within the preset time period when the charging current is stable, calculate the corresponding battery internal resistance based on each battery terminal voltage and the test current, and upload the battery internal resistance, battery terminal voltage, test current, charging data and vehicle information to the server after binding them with the VIN code. The battery health record construction module is used by the server to construct and store the battery health record based on the received battery internal resistance, battery terminal voltage, test current, charging data, vehicle information and VIN code. The horizontal and vertical comparison module is used by the server to perform horizontal and vertical comparisons of the internal resistance of the batteries based on the health records of each battery, and generate fault warning notifications based on the comparison data. The monitoring report push module is used by the server to generate a battery internal resistance monitoring report based on the battery health record and fault warning notification, and push the battery internal resistance monitoring report to the electric vehicle or mobile terminal corresponding to the VIN code.

7. The battery internal resistance online monitoring and early warning system based on charging piles as described in claim 6, characterized in that: The charging module is specifically used for: The charging pile is pre-set with internal resistance monitoring conditions, which include at least the SOC range, charging temperature range, and power margin percentage. After establishing a connection with the electric vehicle via the charging gun, it communicates with the electric vehicle's BMS to obtain the electric vehicle's VIN code and vehicle information; the vehicle information includes at least the vehicle model and battery model. The charging station outputs power based on the output current carried by the received charging command to perform a charging operation on the electric vehicle's power battery.

8. The battery internal resistance online monitoring and early warning system based on charging piles as described in claim 6, characterized in that: The charging data acquisition module is specifically used for: The charging pile collects charging data in real time, including charging voltage, charging current, charging temperature, SOC, and charging timestamp, based on monitoring instructions sent by the server or electric vehicle. It also monitors the current charging conditions and automatically starts the internal resistance monitoring operation when the charging conditions meet the internal resistance monitoring conditions.

9. The battery internal resistance online monitoring and early warning system based on charging piles as described in claim 6, characterized in that: The battery internal resistance calculation module is specifically used for: Within the preset time period corresponding to each of the aforementioned step currents, when the charging current is stable, the battery terminal voltage corresponding to the power battery is collected by listening to the BMS message of the BMS, and a UI curve is plotted based on each of the aforementioned battery terminal voltages and the test current. The corresponding battery internal resistance is calculated based on the slope of the UI curve. After binding the battery internal resistance, battery terminal voltage, test current, charging data, and vehicle information with the VIN code, the data is encrypted and uploaded to the server.

10. The battery internal resistance online monitoring and early warning system based on charging piles as described in claim 6, characterized in that: The horizontal and vertical comparison module is specifically used for: Based on the battery health records, the server performs horizontal comparisons with electric vehicles of the same vehicle model, battery model, charging temperature, and SOC to obtain horizontal comparison data; it also performs vertical comparisons with the battery health records and the historical data of the electric vehicle to obtain vertical comparison data; and, by combining time series analysis or machine learning algorithms, it predicts the internal resistance change trend based on the vertical comparison data, and generates a fault warning notification based on the horizontal comparison data, the vertical comparison data, and the internal resistance change trend.

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