Power battery monitoring management system and method based on Internet

Abstract

The invention relates to the technical field of battery monitoring and management, and discloses a power battery monitoring and management system and method based on the Internet, and the system comprises a data collection unit, a preprocessing unit, a data transmission unit, a feature extraction unit, a data analysis unit, a fault diagnosis unit, and a remote management unit. In battery state evaluation, the charge state is calculated by combining an ampere-hour integral method with an open-circuit voltage method, and errors are calibrated and reduced; the health state is calculated through the internal resistance change rate, maintenance or replacement requirements can be prompted in time, voltage, temperature, charge and health state parameter trends are analyzed through setting observation points, faults including potential faults are efficiently diagnosed in combination with normal threshold value comparison and change rate comparison, and finally a user can remotely check the visual battery state. When a fault occurs, an instruction can be sent remotely, control such as power limiting and balance operation starting is achieved, and convenience and high efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring management, and in particular to an Internet-based power battery monitoring management system and method. Background Art

[0002] With the development of science and technology, power batteries have been widely used in many fields, such as electric vehicles, energy storage systems, etc. The performance and safety of power batteries are crucial to the stable operation of the entire system. In actual use, the state of the power battery will continue to change, and its parameters such as voltage, current, temperature, internal resistance, etc. will be affected by many factors, including the charging and discharging process, ambient temperature, and usage time.

[0003] Traditional power battery monitoring and management methods have many limitations. On the one hand, some monitoring systems can only obtain partial battery data locally and cannot achieve remote monitoring and management. This makes it impossible for users to understand the battery status in time when they are away from the battery system, which brings great inconvenience to the operation and maintenance management of large-scale application scenarios such as distributed energy storage power stations. On the other hand, traditional monitoring methods are not sophisticated enough in data processing, and often lack effective filtering processing for the collected data, which affects the accuracy and reliability of the data, and may affect the accurate judgment of the battery status.

[0004] In addition, in terms of battery status assessment, traditional methods are not accurate enough in calculating the key characteristics of the battery, and lack effective comprehensive analysis methods to accurately evaluate the overall status of the battery. At the same time, most of the possible fault diagnosis of the battery relies on simple threshold judgments or single parameter analysis, which makes it difficult to fully and timely detect potential faults. For example, judging only by a single voltage threshold may ignore the situation where the battery is within the normal voltage range but has other abnormal trends, which may result in the failure to detect the fault in time, thereby affecting the battery's service life and even causing safety issues.

[0005] Against the backdrop of the rapid development of Internet technology, there is an urgent need for a system and method that can combine Internet technology to conduct comprehensive, accurate, and real-time monitoring and management of power batteries, so as to improve the efficiency, safety, and reliability of power batteries. Summary of the invention

[0006] The purpose of the present invention is to provide an Internet-based power battery monitoring management system and method, which solves the technical problems raised in the background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A power battery monitoring and management system based on the Internet, comprising: A data acquisition unit, used to collect various target data from the power battery pack; the target data includes but is not limited to attribute parameters corresponding to voltage, current, temperature, and internal resistance; A preprocessing unit, used for performing data filtering processing on target data; A data transmission unit, used for transmitting the preprocessed target data to the feature extraction unit via an Internet protocol; A feature extraction unit is used to extract features from the preprocessed target data and extract key features of the battery, including the state of charge and health state of the battery; A data analysis unit, used to perform trend analysis based on target data and key features after data filtering and feature extraction, and determine the change trend of the battery; A fault diagnosis unit, used to perform fault diagnosis according to the trend analysis result of the data analysis unit, and determine whether the battery has a fault according to the diagnosis result; The remote management unit is used to realize remote monitoring and management of the power battery; users can view the battery status information through the Internet terminal and perform some remote control operations.

[0008] As a further solution of the present invention: wherein, the target data is obtained by setting sensors at various key nodes of the battery pack.

[0009] As a further solution of the present invention: the data filtering process adopts any one of the median filtering algorithm and the mean filtering algorithm.

[0010] As a further solution of the present invention: the Internet protocol adopts any one of wireless communication technology and wired communication technology, the wireless communication technology includes 4G / 5G, Wi-Fi, and the wired communication technology includes Ethernet.

[0011] As a further solution of the present invention: the data filtering processing method corresponding to the median filtering algorithm is as follows: First, select an attribute parameter corresponding to a time node e and define it as X e , then get the attribute parameter X e Attribute parameters at v time nodes before and after; Among them, X e ∈[U e ,L e ,T e ,R e ], and U e Expressed as the voltage parameter at time node e, L e Expressed as the current parameter at time node e, T e Represented as the temperature parameter at time node e, R e Expressed as the internal resistance parameter at time node e; Then the attribute parameters including the current time node and the v time nodes before and after are used to form a data sequence; The data sequence is {X e-v ,...,X e ,...,X e+v}, and the data sequence contains 2v+1 acquisition time nodes in total; Then the data sequence is sorted from small to large, and the middle value is taken as the attribute parameter after filtering.

[0012] As a further solution of the present invention: the corresponding data filtering processing method in the mean filtering algorithm is as follows: First, select an attribute parameter corresponding to a time node e and define it as X e ; Then get the attribute parameter X e Attribute parameters at v time nodes before and after; Then the attribute parameters including the current time node and the v time nodes before and after are used to form a data sequence; The data sequence is [X e-v ,...,X e ,...,X e+v ], and the data sequence contains 2v+1 acquisition time nodes in total; Then through , calculate the average value XP of all attribute parameters in the data sequence, and then use the average value as the attribute parameter after filtering.

[0013] As a further solution of the present invention: the feature extraction method is as follows: Step 1. Calculation of state of charge The state of charge is obtained by the ampere-hour integration method combined with the open circuit voltage method; Stepv1.1, get the initial state of charge SOC0, the rated capacity C of the battery e and obtaining current parameters in real time during a specified period through a data acquisition unit; The initial state of charge SOC0 is the value determined after the system is started or last calibrated, and the rated capacity C of the battery is e Provided by the battery manufacturer; Step v1.2, divide the specified period into several time intervals with equal time intervals, then extract all current parameters collected in real time in each time interval, and calculate the average value of all current parameters in each time interval, and mark it as L i , i=1, 2, ... n, n represents the number of time intervals; Stepv1.3, through the discretized ampere-hour integral formula: ; Calculate the battery's state of charge value SOC; In the formula, T0 is the time interval of the time interval; Stepv1.4, when the battery reaches equilibrium state, measure its open circuit voltage; Then, the corresponding SOC value is determined by searching a pre-established mapping table of open circuit voltage and SOC for a specific battery type; Among them, this mapping table is the battery characteristic data obtained through a large number of experimental tests, taking the measured open circuit voltage as input and outputting the corresponding SOC value; During battery use, the SOC calculated by the ampere-hour integration method is calibrated regularly using the open circuit voltage method to reduce cumulative errors. Step 2. Calculation of health status The health status is calculated by the rate of change of internal resistance; Step v2.1, obtain the initial internal resistance parameters of the battery measured in a brand new state and the internal resistance parameters measured by the data acquisition unit at the current time node; Stepv2.1, according to the formula: ; Calculate the battery's state of health value SOH; Where R0 is the initial internal resistance parameter of the battery measured in a brand new state, R d is the internal resistance parameter at the current time node; When the SOH value is lower than the corresponding preset threshold, it indicates that the battery is in poor health and may need maintenance or replacement.

[0014] As a further solution of the present invention: the trend analysis method is as follows: Step P1, within the target observation period, set multiple observation time points, and then extract the voltage parameters, temperature parameters, state of charge value and health state value of the battery at each observation time point; StepP2, combine the parameter values ​​of each data type with the observation time points to construct a data point sequence; Step P3, use the least squares method to fit the linear equation y=kx+b, and calculate the slope k and intercept b of the data point sequence corresponding to the voltage parameter, temperature parameter, state of charge value and health state value.

[0015] As a further solution of the present invention: in Step P2, For the voltage parameter, the sequence of data points is as follows: {(G1,U1),(G2,U2),…(G z , U z)}; For the temperature parameter, the sequence of data points is as follows: {(G1,T1),(G2,T2),…(G z , T z )}; For the state of charge value, the sequence of data points is as follows: {(G1,SOC1),(G2,SOC2),…(G z , SOC z )}; For the health status value, the sequence of data points is as follows: {(G1,SOH1),(G2,SOH2),…(G z , SOH z )}; Among them, G1, G2, ... G z They represent the various observation time points within the target observation period and are used to construct data point sequences of the battery's voltage, temperature, state of charge, and health status at different observation time points.

[0016] As a further solution of the present invention: the method in Step P3 is as follows: The data point sequence of the selected voltage parameter corresponding to the data type {(G1, U1), (G2, U2), … (G z , U z )}; Where Gj is x in y=kx+b, U j is y in y=kx+b, j=1, 2, ... z, z represents the number of voltage parameters in the data point sequence, that is, the number of all observation time points in the target observation period; Then through: ; ; Calculate the slope k and intercept b; Similarly, the slope k and intercept b of the temperature parameter, state of charge value, and state of health value are calculated in turn.

[0017] As a further solution of the present invention: wherein the slope represents the direction and rate of change of the trend. When analyzing the trend, the main focus is on the slope, and the intercept is used to assist in understanding the situation of the corresponding data parameter value at the starting point.

[0018] As a further solution of the present invention: the method in Step P3 is as follows: When fitting is performed based on the data point sequence corresponding to the voltage parameter and the slope k is obtained; When k>0, it means that during the target observation period, the battery voltage shows an increasing trend as time goes by; When k = 0, it means that the voltage remains stable during the observation period; When k < 0, it means that the voltage is decreasing as time increases; When fitting is performed based on the data point sequence corresponding to the temperature parameter and the slope k is obtained; When k>0, it indicates that the temperature is increasing with time during the target observation period; When k = 0, it means that the temperature remains constant; When k < 0, it means that the temperature decreases over time; When fitting is performed based on the data point sequence corresponding to the state of charge value and the slope k is obtained; When k>0, it indicates that during the target observation period, the state of charge value is increasing as time goes by, that is, the battery is in the charging process; When k=0, it indicates that the state of charge value remains unchanged, and the battery may be in a stable state where it is neither charged nor discharged; When k < 0, it means that the state of charge value is decreasing, that is, the battery is in a discharging state; When fitting is performed based on the data point sequence corresponding to the health status value and the slope k is obtained; Normally, the value of k is close to 0 during a short observation period, because the battery health status will not change significantly in a short period of time; When k>0, it means there is a measurement error; When k is less than 0, it indicates that the battery health state is declining, and the battery aging is increasing over time.

[0019] As a further solution of the present invention: the fault diagnosis method is as follows: Step D1, extracting the preset normal working threshold range corresponding to the voltage parameter, temperature parameter, state of charge value and health state value; Normal working threshold range includes: normal voltage range, normal temperature range, normal charge state range, and normal health state range; Step D2, compare the voltage parameter, temperature parameter, state of charge value and health state value with their corresponding preset normal working threshold ranges respectively: When the voltage parameter is not within the normal voltage range, it is judged that the battery has an abnormal voltage fault; When the temperature parameter is not within the normal temperature range, it is judged that the battery has an overtemperature fault; When the SOC value is not within the normal SOC range, it indicates that the battery has an over-discharge fault; When the health status value is not within the normal range of health status, it is judged that the battery is seriously aged and faulty.

[0020] As a further solution of the present invention: the fault diagnosis method is also as follows: StepDH1, within the target observation period, extract the parameter values ​​corresponding to each data type at the start observation time point and the end observation time point; That is, extract the voltage parameters corresponding to the data point sequence (G1, U1) and (G z , U z ), temperature parameters correspond to the data point sequence (G1, T1) and (G z , T z ), the state of charge value corresponds to the data point sequence (G1, SOC1) and (G z , SOC z ), the health status value corresponds to the data point sequence (G1, SOH1) and (G z , SOH z ); Step DH2, select the voltage parameter corresponding to the data point sequence (G1, U1) and (G z , U z ); Then through: ; Calculate the rate of change of voltage parameters during the target observation period BHL U ; Similarly, the change rates of temperature parameters, state of charge values, and health status values ​​during the target observation period are calculated in turn; Step DH3, then compare the change rates of the voltage parameter, temperature parameter, state of charge value, and health state value during the target observation period with their corresponding preset change rate thresholds: If at least one of the comparison formulas is true, including the voltage parameter being greater than its corresponding preset rate of change threshold, the temperature parameter being greater than its corresponding preset rate of change threshold, the state of charge value being greater than its corresponding preset rate of change threshold, and the health status value being greater than its corresponding preset rate of change threshold, then it is determined that the battery has a potential fault.

[0021] As a further solution of the present invention, the management process is as follows: The server stores the processed battery status information in a database and provides data query services to remote terminals through a network interface; The remote terminal obtains data by sending HTTP requests and displays it to the user in a visual way; The remote control operations are as follows: When a fault is detected, the user sends a remote command, and after receiving the command, the server sends the command to the battery management system through the data transmission unit to perform the corresponding operation; The instructions include but are not limited to limiting battery charging and discharging power and starting battery balancing operations.

[0022] A power battery monitoring and management method based on the Internet, the method is implemented by a power battery monitoring and management system based on the Internet, and the method comprises the following steps: Step 1: Data Collection Collect various target data from the power battery pack; Step 2: Preprocessing Perform data filtering on the target data; Step 3: Feature Extraction Perform feature extraction on the preprocessed target data and extract the key features of the battery Step 4: Data Analysis Perform trend analysis based on target data and key features after data filtering and feature extraction, and determine the battery change trend; Step 5: Fault Diagnosis Perform fault diagnosis based on the trend analysis results of the data analysis unit, and determine whether the battery has a fault based on the diagnosis results; Step 6: Remote Management Remote monitoring and management of power batteries.

[0023] Beneficial effects of the present invention: Comprehensive and accurate data collection: By setting sensors at various key nodes of the battery pack to obtain a variety of target data, including corresponding attribute parameters such as voltage, current, temperature, internal resistance, etc., the operating status information of the power battery can be fully and accurately grasped, providing a rich data foundation for subsequent analysis, diagnosis and management.

[0024] Effective data filtering: Using median filtering algorithm or mean filtering algorithm to filter the collected target data can effectively remove noise and interference in the data, improve the accuracy and stability of the data, and make subsequent analysis and judgment based on this data more reliable.

[0025] Accurate state of charge calculation: The state of charge is calculated by the ampere-hour integration method combined with the open circuit voltage method. It not only collects current parameters in real time and calculates using the ampere-hour integration formula, but also calibrates the battery's state of charge by combining the open circuit voltage and SOC mapping table after the battery reaches a static equilibrium state. This can more accurately determine the battery's state of charge, help to reasonably arrange the battery's charging and discharging operations, and extend the battery life.

[0026] Reliable health status assessment: The health status is calculated by the internal resistance change rate. By comparing the internal resistance parameters of the battery in a new state and at the current time point, the health status of the battery can be understood in a timely and accurate manner. When the SOH value is lower than the preset threshold, it can be prompted that maintenance or replacement may be required, effectively avoiding failures caused by battery aging and other problems.

[0027] Accurate trend analysis: By setting multiple observation time points within the target observation period, extracting parameter values ​​of different data types and constructing a data point sequence, and then using the least squares method to fit the linear equation to calculate the slope and intercept of the data point sequence corresponding to each parameter, the changing trends of battery voltage, temperature, state of charge and health status can be clearly analyzed, which helps to discover potential problems in advance and take corresponding measures.

[0028] Efficient fault diagnosis: On the one hand, by comparing each parameter with the preset normal operating threshold range, it can quickly determine whether the battery has faults such as abnormal voltage, high temperature, over-discharge, severe aging, etc.; on the other hand, by calculating the change rate of each parameter during the target observation period and comparing it with the preset change rate threshold, it can further determine whether the battery has potential faults, thereby achieving comprehensive and efficient fault diagnosis and ensuring safe and reliable operation of the battery.

[0029] Convenient remote monitoring: Users can view battery status information through Internet terminals. The server stores the processed battery status information in the database and provides data query services through the network interface. The remote terminal obtains data by sending HTTP requests and displays it to the user in a visual manner, allowing users to easily understand the real-time status of the battery anytime and anywhere.

[0030] Flexible remote control: When a fault is detected, the user can send instructions remotely. After receiving the instructions, the server sends the instructions to the battery management system through the data transmission unit to perform corresponding operations, such as limiting the battery charging and discharging power, starting battery balancing operations, etc., realizing remote and flexible management of power batteries, improving management efficiency and the ability to cope with faults.

[0031] In summary, the Internet-based power battery monitoring and management system and method of the present invention can comprehensively and accurately monitor and manage power batteries, effectively improve the safety, reliability and service life of batteries, and at the same time provide users with convenient remote monitoring and management means. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below in conjunction with the accompanying drawings.

[0033] Figure 1 It is a system block diagram of an Internet-based power battery monitoring management system and method of the present invention.

[0034] Figure 2 It is a flow chart of an Internet-based power battery monitoring management system and method of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1

[0036] See also Figure 1 and Figure 2 As shown, the present invention is an Internet-based power battery monitoring and management system, comprising: A data acquisition unit, used to collect various target data from the power battery pack; Target data includes but is not limited to attribute parameters corresponding to voltage, current, temperature, and internal resistance; The target data is obtained by setting sensors at each key node of the battery pack; A data transmission unit, used for transmitting the collected target data to the feature extraction unit via the Internet protocol; It adopts any one of wireless communication technology and wired communication technology, the wireless communication technology includes 4G / 5G, Wi-Fi, and the wired communication technology includes Ethernet; A feature extraction unit is used to extract features from the target data and extract key features of the battery, including the state of charge and health of the battery; The specific extraction method is as follows: Step 1. Calculation of state of charge The state of charge is obtained by the ampere-hour integration method combined with the open circuit voltage method; Stepv1.1, get the initial state of charge SOC0, the rated capacity C of the battery e and obtaining current parameters in real time during a specified period through a data acquisition unit; The initial state of charge SOC0 is the value determined after the system is started or last calibrated, and the rated capacity C of the battery is e Provided by the battery manufacturer; Step v1.2, divide the specified period into several time intervals with equal time intervals, then extract all current parameters collected in real time in each time interval, and calculate the average value of all current parameters in each time interval, and mark it as L i , i=1, 2, ... n, n represents the number of time intervals; Stepv1.3, through the discretized ampere-hour integral formula: ; Calculate the battery's state of charge value SOC; In the formula, T0 is the time interval of the time interval; Stepv1.4, when the battery reaches equilibrium state, measure its open circuit voltage; Then, the corresponding SOC value is determined by searching a pre-established mapping table of open circuit voltage and SOC for a specific battery type; Among them, this mapping table is the battery characteristic data obtained through a large number of experimental tests, taking the measured open circuit voltage as input and outputting the corresponding SOC value; During battery use, the SOC calculated by the ampere-hour integration method is calibrated regularly using the open circuit voltage method to reduce cumulative errors. Step 2. Calculation of health status The health status is calculated by the rate of change of internal resistance; Step v2.1, obtain the initial internal resistance parameters of the battery measured in a brand new state and the internal resistance parameters measured by the data acquisition unit at the current time node; Stepv2.1, according to the formula: ; Calculate the battery's state of health value SOH; Where R0 is the initial internal resistance parameter of the battery measured in a brand new state, R d is the internal resistance parameter at the current time node; For example: if R0=0.5Ω, the internal resistance parameter R at the current time node is d =0.6Ω, then ; Generally speaking, when the SOH value is lower than the corresponding preset threshold, it indicates that the battery is in poor health and may need maintenance or replacement. In this example, the battery's health status SOH is 120%, that is, the SOH value is too high, which may be a measurement error or a special case. Generally, the normal range has certain limitations, and the SOH should gradually decrease as the battery ages. A data analysis unit, used to perform trend analysis based on target data and key features after data filtering and feature extraction, and determine the change trend of the battery; The trend analysis method is as follows: Step P1, within the target observation period, set multiple observation time points, and then extract the voltage parameters, temperature parameters, state of charge value and health state value of the battery at each observation time point; StepP2, combine the parameter values ​​of each data type with the observation time points to construct a data point sequence; For the voltage parameter, the sequence of data points is as follows: {(G1,U1),(G2,U2),…(G z , U z )}; For the temperature parameter, the sequence of data points is as follows: {(G1,T1),(G2,T2),…(G z , T z )}; For the state of charge value, the sequence of data points is as follows: {(G1,SOC1),(G2,SOC2),…(G z , SOC z )}; For the health status value, the sequence of data points is as follows: {(G1,SOH1),(G2,SOH2),…(G z , SOH z )}; Among them, G1, G2, ... G z They represent the various observation time points within the target observation period and are used to construct data point sequences of the battery's voltage, temperature, state of charge, and health status at different observation time points; Step P3, use the least squares method to fit the linear equation y=kx+b, and calculate the slope k and intercept b of the data point sequence corresponding to the voltage parameter, temperature parameter, state of charge value and health state value; The specific method is as follows: The data point sequence of the selected voltage parameter corresponding to the data type {(G1, U1), (G2, U2), … (G z , U z )}; Where Gj is x in y=kx+b, U j is y in y=kx+b, j=1, 2, ... z, z represents the number of voltage parameters in the data point sequence, that is, the number of all observation time points in the target observation period; Then through: ; ; Calculate the slope k and intercept b; Among them, the slope indicates the direction and rate of change of the trend. When analyzing the trend, the main focus is on the slope, and the intercept is used to assist in understanding the situation of the corresponding data parameter value at the starting point; When fitting is performed based on the data point sequence corresponding to the voltage parameter and the slope k is obtained; When k>0, for example, k=0.1, it means that during the target observation period, the battery voltage shows an upward trend over time, which may be due to the battery being in a charging state or some abnormality inside the battery causing the voltage to rise abnormally; When k = 0, it means that the voltage remains stable during the observation period, which may be the case that the battery is in a static or stable working state and there is no charging or discharging activity; When k is less than 0, for example, k = -0.05, it means that the voltage is decreasing with time, which may be due to the battery being discharged or leaking. When fitting is performed based on the data point sequence corresponding to the temperature parameter and the slope k is obtained; When k>0, for example, k=0.5, it indicates that during the target observation period, the temperature increases with time, which may be the early performance of the battery working under high load, poor heat dissipation, or thermal runaway; When k=0, it means that the temperature remains constant, and the battery's thermal management system may work normally to stabilize the temperature at a certain value; When k is less than 0, for example, k = -0.2, it means that the temperature decreases over time, which may be due to the decrease in ambient temperature or the significant effect of the battery cooling measures. However, if the temperature drops too quickly, it may also indicate other problems with the battery, such as sensor failure or abnormal thermal management inside the battery. When fitting is performed based on the data point sequence corresponding to the state of charge value and the slope k is obtained; When k>0, for example, k=0.02, it indicates that during the target observation period, the state of charge value is increasing as time goes by, that is, the battery is in the charging process; When k=0, it indicates that the state of charge value remains unchanged, and the battery may be in a stable state where it is neither charged nor discharged; When k is less than 0, for example, k = -0.03, it means that the state of charge value is decreasing, that is, the battery is in a discharging state. If the SOC decreases too quickly, it may indicate that the battery is in an abnormal discharge state, such as a short circuit or excessive power consumption by a high-energy-consuming device. When fitting is performed based on the data point sequence corresponding to the health status value and the slope k is obtained; Normally, the value of k is close to 0 during a short observation period, because the battery health status will not change significantly in a short period of time; When k>0, it means that there is a measurement error or a special situation occurs, such as the battery has been repaired. When k is less than 0, for example, k = -0.05, it indicates that the battery health is declining. As time goes by, the battery ages more rapidly, which may be caused by long-term use, high temperature environment, or frequent deep charge and discharge. A fault diagnosis unit, used to perform fault diagnosis according to the trend analysis result of the data analysis unit, and determine whether the battery has a fault according to the diagnosis result; The fault diagnosis method is as follows: Step D1, extracting the preset normal working threshold range corresponding to the voltage parameter, temperature parameter, state of charge value and health state value; Normal working threshold range includes: normal voltage range, normal temperature range, normal charge state range, and normal health state range; Step D2, compare the voltage parameter, temperature parameter, state of charge value and health state value with their corresponding preset normal working threshold ranges respectively: When the voltage parameter is not within the normal voltage range, it is judged that the battery has an abnormal voltage fault; When the temperature parameter is not within the normal temperature range, it is judged that the battery has an overtemperature fault; When the SOC value is not within the normal SOC range, it indicates that the battery has an over-discharge fault; When the health status value is not within the normal range of health status, it is judged that the battery is seriously aged and faulty; Remote management unit, used to realize remote monitoring and management of power batteries; users can view battery status information through the Internet terminal and perform some remote control operations; The management process is as follows: The server stores the processed battery status information in a database and provides data query services to remote terminals through a network interface; The remote terminal obtains data by sending HTTP requests and displays it to the user in a visual way; The remote control operations are as follows: When a fault is detected, the user can send a command remotely. After receiving the command, the server sends the command to the battery management system through the data transmission unit to perform the corresponding operation. The instructions include but are not limited to limiting battery charging and discharging power and starting battery balancing operations.

[0037] In the first embodiment of the present invention, a data acquisition unit can collect a variety of target data from the power battery pack, and the data transmission unit can use a variety of communication technologies to transmit the data to the subsequent units, thereby ensuring the comprehensive acquisition and effective transmission of the battery data, and providing a rich data basis for subsequent analysis and management; the feature extraction unit can accurately extract the key features of the battery using a specific calculation method, which is helpful to accurately grasp the working status of the battery; the data analysis unit performs trend analysis based on the target data and key features after data filtering and feature extraction, and uses the least squares method to fit the linear equation to determine the change trend of each parameter, so as to timely discover the problems that may exist in the battery under different states such as charging, discharging, and standing, such as abnormal voltage increase, abnormal temperature change, over-discharge, etc.; the fault diagnosis unit performs fault diagnosis based on the trend analysis results, and can accurately determine whether the battery has a fault by comparing with the preset normal working threshold range; the remote management unit realizes remote monitoring and management of the power battery, and the user can view the battery status information and perform remote control operations through the Internet terminal, which facilitates the management and maintenance of the battery. Embodiment 2

[0038] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the second embodiment is different from that of the first embodiment only in that: The fault diagnosis method is as follows: StepDH1, within the target observation period, extract the parameter values ​​corresponding to each data type at the start observation time point and the end observation time point; That is, extract the voltage parameters corresponding to the data point sequence (G1, U1) and (G z , U z ), temperature parameters correspond to the data point sequence (G1, T1) and (G z , T z ), the state of charge value corresponds to the data point sequence (G1, SOC1) and (G z , SOC z ), the health status value corresponds to the data point sequence (G1, SOH1) and (G z , SOH z ); Step DH2, select the voltage parameter corresponding to the data point sequence (G1, U1) and (G z , U z ); Then through: ; Calculate the rate of change of voltage parameters during the target observation period BHL U ; Similarly, the change rates of temperature parameters, state of charge values, and health status values ​​during the target observation period are calculated in turn; Step DH3, then compare the change rates of the voltage parameter, temperature parameter, state of charge value, and health state value during the target observation period with their corresponding preset change rate thresholds: If at least one of the comparison formulas is true, including the voltage parameter being greater than its corresponding preset rate of change threshold, the temperature parameter being greater than its corresponding preset rate of change threshold, the state of charge value being greater than its corresponding preset rate of change threshold, and the health status value being greater than its corresponding preset rate of change threshold, then it is determined that the battery has a potential fault.

[0039] Based on the first embodiment, the second embodiment adds a new fault diagnosis method. By calculating the change rate of each data type in the target observation period and comparing it with the corresponding preset change rate threshold, if at least one comparison formula is established, it can be determined that the battery has a potential fault. This method further enriches the means of fault diagnosis, can more sensitively capture potential problems that may occur in the battery, and improves the comprehensiveness and accuracy of fault diagnosis. Embodiment 3

[0040] As the third embodiment of the present invention, when the present application is implemented, compared with the first and second embodiments, the technical solution of the third embodiment is to combine the solutions of the first and second embodiments, and the difference between the technical solution of the third embodiment and the first and second embodiments is only in the third embodiment; A preprocessing unit, used for performing data filtering processing on target data; The data filtering process is as follows: Data filtering uses median filtering algorithm; First, select an attribute parameter corresponding to a time node e and define it as X e , then get the attribute parameter X e Attribute parameters at v time nodes before and after; Among them, X e ∈[U e ,L e ,T e ,R e ], and U e Expressed as the voltage parameter at time node e, L e Expressed as the current parameter at time node e, T e Represented as the temperature parameter at time node e, R e Expressed as the internal resistance parameter at time node e; Then the attribute parameters including the current time node and the v time nodes before and after are used to form a data sequence; The data sequence is {X e-v ,...,X e ,...,X e+v}, and the data sequence contains 2v+1 acquisition time nodes in total; Then the data sequence is sorted from small to large, and the middle value is taken as the attribute parameter after filtering.

[0041] Compared with the first and second embodiments, the third embodiment adds a preprocessing unit to perform median filtering on the target data. The median filtering algorithm can effectively remove interference factors such as noise in the data, making the collected data smoother and more accurate, thereby providing a more reliable data basis for subsequent feature extraction, trend analysis, fault diagnosis and other operations, and improving the accuracy of the entire system in judging the battery status. Embodiment 4

[0042] As the fourth embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the second embodiment and the third embodiment, the difference between this embodiment and the first embodiment, the second embodiment and the third embodiment is only that in this fourth embodiment, the data filtering processing method is also as follows: The data filtering process also uses a mean filtering algorithm; First, select an attribute parameter corresponding to a time node e and define it as X e ; Then get the attribute parameter X e Attribute parameters at v time nodes before and after; Then the attribute parameters including the current time node and the v time nodes before and after are used to form a data sequence; The data sequence is [X e-v ,...,X e ,...,X e+v ], and the data sequence contains 2v+1 acquisition time nodes in total; Then through , calculate the average value XP of all attribute parameters in the data sequence, and then use the average value as the attribute parameter after filtering.

[0043] Based on the third embodiment, the fourth embodiment further enriches the data filtering processing method. In addition to the median filtering algorithm, the mean filtering algorithm is also used. The mean filtering algorithm calculates the average value of all attribute parameters in the data sequence as the attribute parameter after filtering, which can also play the role of smoothing data and reducing interference. Combined with the median filtering algorithm, it provides more options for data preprocessing. A more appropriate filtering method can be selected according to different application scenarios and data characteristics, further improving data quality, and thus improving the performance of the entire system. Embodiment 5

[0044] As the fifth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second, third and fourth embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second, third and fourth embodiments.

[0045] The fifth embodiment combines the solutions of the first, second, third and fourth embodiments to integrate the advantages of each embodiment, including comprehensive and accurate battery data collection and transmission, multi-mode battery key feature extraction, in-depth and detailed battery trend analysis, multiple fault diagnosis methods and effective data preprocessing, thereby achieving more comprehensive, accurate and efficient monitoring and management of power batteries, and better meeting the needs of power battery status monitoring and management in different application scenarios.

[0046] The present invention also provides an Internet-based power battery monitoring and management method, which is implemented by an Internet-based power battery monitoring and management system, and the method includes the following steps: Step 1: Data Collection Collect various target data from the power battery pack; Step 2: Preprocessing Perform data filtering on the target data; Step 3: Feature Extraction Perform feature extraction on the preprocessed target data and extract the key features of the battery Step 4: Data Analysis Perform trend analysis based on target data and key features after data filtering and feature extraction, and determine the battery change trend; Step 5: Fault Diagnosis Perform fault diagnosis based on the trend analysis results of the data analysis unit, and determine whether the battery has a fault based on the diagnosis results; Step 6: Remote Management Remote monitoring and management of power batteries.

[0047] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0048] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A power battery monitoring and management system based on the Internet, characterized in that: include: A data acquisition unit, used to collect various target data from the power battery pack; Target data includes but is not limited to attribute parameters corresponding to voltage, current, temperature, and internal resistance; A preprocessing unit, used for performing data filtering processing on target data; A data transmission unit, used for transmitting the preprocessed target data to the feature extraction unit via an Internet protocol; The feature extraction unit is used to extract features from the preprocessed target data and extract key features of the battery. The key features include the state of charge and health state of the battery. The feature extraction method is as follows: Step 1. Calculation of state of charge The state of charge is obtained by the ampere-hour integration method combined with the open circuit voltage method; Stepv1.1, get the initial state of charge SOC0, the rated capacity C of the battery e and obtaining current parameters in real time during a specified period through a data acquisition unit; The initial state of charge SOC0 is the value determined after the system is started or last calibrated, and the rated capacity C of the battery is e Provided by the battery manufacturer; Step v1.2, divide the specified period into several time intervals with equal time intervals, then extract all the current parameters collected in real time in each time interval, and calculate the average value of all the current parameters in each time interval, and mark it as L i , i=1, 2, ... n, n represents the number of time intervals; Stepv1.3, through the discretized ampere-hour integral formula: ； Calculate the battery's state of charge value SOC; In the formula, T0 is the time interval of the time interval; Stepv1.4, when the battery reaches equilibrium state, measure its open circuit voltage; Then, the corresponding SOC value is determined by searching a pre-established mapping table of open circuit voltage and SOC for a specific battery type; Step 2. Calculation of health status The health status is calculated by the rate of change of internal resistance; Step v2.1, obtain the initial internal resistance parameters of the battery measured in a brand new state and the internal resistance parameters measured by the data acquisition unit at the current time node; Stepv2.1, according to the formula: ； Calculate the battery's state of health value SOH; Where R0 is the initial internal resistance parameter of the battery measured in a brand new state, R d is the internal resistance parameter at the current time node; A data analysis unit, used to perform trend analysis based on target data and key features after data filtering and feature extraction, and determine the change trend of the battery; A fault diagnosis unit, used to perform fault diagnosis according to the trend analysis result of the data analysis unit, and determine whether the battery has a fault according to the diagnosis result; The remote management unit is used for users to remotely monitor and manage the power battery.

2. The Internet-based power battery monitoring and management system according to claim 1, characterized in that: The trend analysis method is as follows: Step P1, within the target observation period, set multiple observation time points, and then extract the voltage parameters, temperature parameters, state of charge value and health state value of the battery at each observation time point; StepP2, combine the parameter values ​​of each data type with the observation time points to construct a data point sequence; Step P3, use the least squares method to fit the straight line equation y=kx+b, and calculate the slope k and intercept b of the data point sequence corresponding to the voltage parameter, temperature parameter, state of charge value and health state value. The slope indicates the direction and rate of change of the trend.

3. The Internet-based power battery monitoring and management system according to claim 1 is characterized in that: In StepP2, For the voltage parameter, the sequence of data points is as follows: {(G1,U1),(G2,U2),……(G z ,U z )}; For the temperature parameter, the sequence of data points is as follows: {(G1,T1),(G2,T2),……(G z ,T z )}; For the state of charge value, the sequence of data points is as follows: {(G1,SOC1),(G2,SOC2),……(G z ,SOC z )}; For the health status value, the sequence of data points is as follows: {(G1,SOH1),(G2,SOH2),……(G z ,SOH z )}; Among them, G1, G2, ... G z They represent the various observation time points within the target observation period and are used to construct a data point sequence of the battery's voltage, temperature, state of charge value, and health state value at different observation time points.

4. The Internet-based power battery monitoring and management system according to claim 3 is characterized in that: The method in StepP3 is as follows: The data point sequence of the selected voltage parameter corresponding to the data type {(G1, U1), (G2, U2), … (G z , U z )}; Where Gj is x in y=kx+b, U j is y in y=kx+b, j=1, 2, ... z, z represents the number of voltage parameters in the data point sequence, that is, the number of all observation time points in the target observation period; Then through: ； ； Calculate the slope k and intercept b; Similarly, the slope k and intercept b of the temperature parameter, state of charge value, and health state value are calculated in turn; in: When fitting is performed based on the data point sequence corresponding to the voltage parameter and the slope k is obtained; When k>0, it means that during the target observation period, the battery voltage shows an increasing trend as time goes by; When k = 0, it means that the voltage remains stable during the observation period; When k < 0, it means that the voltage is decreasing as time increases; When fitting is performed based on the data point sequence corresponding to the temperature parameter and the slope k is obtained; When k>0, it indicates that the temperature is increasing with time during the target observation period; When k = 0, it means that the temperature remains constant; When k < 0, it means that the temperature decreases over time; When fitting is performed based on the data point sequence corresponding to the state of charge value and the slope k is obtained; When k>0, it indicates that during the target observation period, the state of charge value is increasing as time goes by, that is, the battery is in the charging process; When k=0, it indicates that the state of charge value remains unchanged, and the battery may be in a stable state where it is neither charged nor discharged; When k < 0, it means that the state of charge value is decreasing, that is, the battery is in a discharging state; When fitting is performed based on the data point sequence corresponding to the health status value and the slope k is obtained; When k>0, it means there is a measurement error; When k < 0, it indicates that the battery health state is declining.

5. The Internet-based power battery monitoring and management system according to claim 1, characterized in that: The fault diagnosis method is as follows: Step D1, extracting the preset normal working threshold range corresponding to the voltage parameter, temperature parameter, state of charge value and health state value; Normal working threshold range includes: normal voltage range, normal temperature range, normal charge state range, and normal health state range; Step D2, compare the voltage parameter, temperature parameter, state of charge value and health state value with their corresponding preset normal working threshold ranges, and determine whether the battery is faulty based on the comparison results.

6. The Internet-based power battery monitoring and management system according to claim 5, characterized in that: The fault diagnosis method is as follows: StepDH1, within the target observation period, extract the parameter values ​​corresponding to each data type at the start observation time point and the end observation time point; That is, extract the voltage parameters corresponding to the data point sequence (G1, U1) and (G z , U z ), temperature parameters correspond to the data point sequence (G1, T1) and (G z , T z ), the state of charge value corresponds to the data point sequence (G1, SOC1) and (G z , SOC z ), the health status value corresponds to the data point sequence (G1, SOH1) and (G z , SOH z ); Step DH2, select the voltage parameter corresponding to the data point sequence (G1, U1) and (G z , U z ); Then through: ； Calculate the rate of change of voltage parameters during the target observation period BHL U ; Similarly, the change rates of temperature parameters, state of charge values, and health status values ​​during the target observation period are calculated in turn; Step DH3, then compare the change rates of the voltage parameter, temperature parameter, state of charge value, and health state value during the target observation period with their corresponding preset change rate thresholds, and determine whether the battery is faulty based on the comparison results.

7. The Internet-based power battery monitoring and management system according to claim 6, characterized in that: In Step D2: When the voltage parameter is not within the normal voltage range, it is judged that the battery has an abnormal voltage fault; When the temperature parameter is not within the normal temperature range, it is judged that the battery has an overtemperature fault; When the SOC value is not within the normal SOC range, it indicates that the battery has an over-discharge fault; When the health status value is not within the normal range of health status, it is judged that the battery is seriously aged and faulty; In StepDH3, If at least one of the comparison formulas is true, including the voltage parameter being greater than its corresponding preset rate of change threshold, the temperature parameter being greater than its corresponding preset rate of change threshold, the state of charge value being greater than its corresponding preset rate of change threshold, and the health status value being greater than its corresponding preset rate of change threshold, then it is determined that the battery has a potential fault.

8. The Internet-based power battery monitoring and management system according to claim 1, characterized in that: Data filtering processing adopts any one of the median filtering algorithm and the mean filtering algorithm; The specific method is as follows: The data filtering processing method corresponding to the median filtering algorithm is as follows: First, select an attribute parameter corresponding to a time node e and define it as X e , then get the attribute parameter X e Attribute parameters at v time nodes before and after; Among them, X e ∈[U e ,L e ,T e ,R e ], and U e Expressed as the voltage parameter at time node e, L e Expressed as the current parameter at time node e, T e Represented as the temperature parameter at time node e, R e Expressed as the internal resistance parameter at time node e; Then the attribute parameters including the current time node and the v time nodes before and after are used to form a data sequence; The data sequence is {X e-v ,...,X e ,...,X e+v }, and the data sequence contains 2v+1 acquisition time nodes in total; When the median filter algorithm is used, the data sequence is sorted from small to large, and then the middle value is taken as the attribute parameter after filtering; When the mean filtering algorithm is used, the average value XP of all attribute parameters in the data sequence is calculated, and then the average value is used as the attribute parameter after filtering.

9. A power battery monitoring and management method based on the Internet, characterized in that: The method is implemented by an Internet-based power battery monitoring and management system as described in any one of claims 1-8.

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