A battery type identification and detection method and system based on charge and discharge characteristics

Abstract

The present invention relates to the field of battery detection technology, and more specifically, to a battery type identification and detection method and system based on charge and discharge characteristics. The solution includes real-time acquisition of charge and discharge information of each battery; determining the battery platform based on the charge and discharge information, wherein the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery; calculating the slope characteristics of the current battery in real time, and judging the similarity between the current battery and the preset battery type, and selecting the battery type with the highest similarity as the matching result; extracting abnormal jump information inside the battery in real time and displaying it; analyzing the current state of the battery based on the changes in current and voltage; monitoring the battery temperature in real time and prompting an overheating state. The solution judges the battery type by integrating the voltage platform, slope characteristics, and jump characteristics, and performs a joint diagnosis. The combination of multiple features can analyze from different dimensions, greatly improving the recognition accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and more specifically, to a method and system for identifying and detecting battery types based on charge and discharge characteristics. Background Art

[0002] In the field of battery testing, battery type identification and detection based on charge and discharge characteristics plays an extremely critical role. Currently, various types of batteries are widely used in many fields, from everyday electronic devices to large-scale applications such as electric vehicles and energy storage systems. Different types of batteries, such as lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries, have significantly different charge and discharge characteristics. By analyzing the battery's voltage, current, time, and capacity changes during the charge and discharge process, it is possible to accurately identify the battery type and effectively detect key parameters such as the battery's health status and remaining capacity. This detection method based on charge and discharge characteristics not only provides a basis for the rational use of batteries and avoids equipment damage and performance degradation caused by the incorrect use of battery types, but also plays a core role in battery management systems, optimizing battery charge and discharge strategies, extending battery life, and improving energy utilization efficiency.

[0003] Prior to the technology of the present invention, the existing battery type identification and detection methods based on charge and discharge characteristics mainly made judgments by monitoring the basic parameters of the battery charge and discharge process, such as the curves of voltage and current changing over time. Some methods use simple threshold comparisons to compare the voltage and current values at a specific stage with pre-set standard values to identify the battery type and evaluate the status. However, this method has many difficulties and key points. The difficulty lies in the fact that the charge and discharge curves of different battery types have a certain degree of similarity, especially under some working conditions, and it is very easy to make misjudgments based on simple parameter comparisons. In addition, in actual use, the battery will be affected by various factors such as ambient temperature and charge and discharge rate, resulting in fluctuations in the charge and discharge characteristics, further increasing the difficulty of accurate identification. Summary of the Invention

[0004] In view of the above problems, the present invention proposes a battery type identification and detection method and system based on charge and discharge characteristics. The battery type is judged by integrating the voltage platform, slope characteristics and jump characteristics, and a joint diagnosis is performed. The combination of multiple features can analyze from different dimensions, greatly improving the recognition accuracy and more comprehensively and accurately distinguishing between ternary lithium, lead-acid and lithium iron phosphate batteries.

[0005] According to a first aspect of an embodiment of the present invention, a method for identifying and detecting battery types based on charge and discharge characteristics is provided.

[0006] In one or more embodiments, preferably, the battery type identification and detection method based on charge and discharge characteristics includes:

[0007] Get the charge and discharge information of each battery in real time;

[0008] Determining a battery platform based on the charge and discharge information, where the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery;

[0009] Calculate the slope characteristics of the current battery in real time, determine the similarity between the current battery and the preset battery type, and select the battery type with the highest similarity as the matching result;

[0010] Extract and display abnormal jump information inside the battery in real time;

[0011] Analyze the current battery status based on the changes in current and voltage, including charging, discharging, and balancing.

[0012] Monitor battery temperature in real time and indicate overheating.

[0013] In one or more embodiments, preferably, the real-time acquisition of charge and discharge information of each battery specifically includes:

[0014] Collect voltage and current signals during battery charging and discharging;

[0015] Transmit real-time data to the data center for recording at a frequency of 10,000 times per second;

[0016] Obtain the curve of battery voltage and charging current changing with time.

[0017] In one or more embodiments, preferably, determining a battery platform based on the charge and discharge information, wherein the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery, specifically includes:

[0018] Get the voltage value of each battery;

[0019] Set the acquisition window, each window includes the latest n data points;

[0020] Set m consecutive windows;

[0021] The average value μ of the data in each window i and standard deviation σ i ;

[0022] Setting σ max is the maximum standard deviation allowed within each window;

[0023] Set E to the maximum fluctuation range allowed for the average value;

[0024] Determine whether the first calculation formula is satisfied;

[0025] Determining whether the second calculation formula is satisfied;

[0026] If both the first and second calculation formulas are satisfied, the voltage value at the current moment is considered to be a plateau voltage. Otherwise, the determination is continued until a plateau voltage appears.

[0027] If the platform voltage is between 3.6V and 4.2V, it is considered to be a ternary lithium battery charging platform. If the platform voltage is between 2.3V-2.4V, it is considered to be a lead-acid battery charging platform. If the platform voltage is between 3.2V-3.4V, it is considered to be a lithium iron phosphate battery charging platform.

[0028] The first calculation formula is:

[0029]

[0030] Among them, σ j is the standard deviation of the jth window, M is the total number of consecutive windows, and j is the window number;

[0031] The second calculation formula is:

[0032]

[0033] Among them, max() is the maximum value selection module, min() is the minimum value selection module, μ M is the average value of the data in the Mth window, and E is the maximum fluctuation range allowed for the average value.

[0034] In one or more embodiments, preferably, the real-time calculation of the slope characteristics of the current battery, determining the similarity between the current battery and a preset battery type, and selecting the battery type with the highest similarity as the matching result specifically includes:

[0035] Calculate the linear regression slope using the third calculation formula;

[0036] Calculate the slope similarity using the fourth calculation formula according to the linear regression slopes of the two data sets;

[0037] The slope change rate is calculated using the fifth calculation formula. When the slope change rate is greater than a preset value, it is determined to be the peak start time, and the maximum value of the data value after the peak start is extracted as the peak highest point;

[0038] The peak height is calculated using the sixth calculation formula,

[0039] Using the eighth calculation formula to determine the peak similarity between the current battery and the preset battery type;

[0040] Calculate the comprehensive quantitative similarity using the ninth calculation formula;

[0041] Calculate the battery type with the highest comprehensive similarity to the current battery as the matching result;

[0042] The third calculation formula is:

[0043]

[0044] Where m is the linear regression slope, x i is the time point, y i is the data value, n is the data amount;

[0045] The fourth calculation formula is:

[0046]

[0047] Wherein, S12 is the slope similarity of the data set, m1 and m2 are the linear regression slopes of the data set respectively;

[0048] The fifth calculation formula is:

[0049]

[0050] Among them, △m i is the slope change rate at the i-th data moment, x i is the i-th time point, y i is the i-th data value, x i+1 is the i+1th time point, y i+1 is the i+1th data value;

[0051] The sixth calculation formula is:

[0052] H=max(ypeak-ybase)

[0053] Where H is the peak height, ypeak is the highest point of the peak, and ybase is the data value at the time when the peak starts;

[0054] The seventh calculation formula is:

[0055] T=tend-tstart

[0056] Among them, T is the duration, tend is the moment of the peak's highest point, and tstart is the moment when the peak starts;

[0057] The eighth calculation formula is:

[0058]

[0059] Among them, S Fis the peak similarity, H1 and H2 are the peak heights of the current battery and the preset battery type respectively, and T1 and T2 are the durations of the current battery and the preset battery type respectively;

[0060] The ninth calculation formula is:

[0061] S Z =w1×S 12 +w2×S F

[0062] Among them, S Z is the comprehensive similarity, w1 and w2 are the first and second weight coefficients respectively.

[0063] In one or more embodiments, preferably, the real-time extraction and display of abnormal jump information inside the battery specifically includes:

[0064] Observe whether there is voltage jump in the voltage-time waveform;

[0065] If the amplitude is the same or similar to the preset jump threshold, it is considered that there is a battery abnormality, and the waveform showing the abnormal jump process is marked.

[0066] In one or more embodiments, preferably, analyzing the current state of the battery based on the changes in current and voltage, where the current state includes charging, discharging, and balancing, specifically includes:

[0067] If the current direction of the battery is from the outside to the inside and the voltage continues to increase, it is considered to be in the charging state;

[0068] If the battery current direction is from the inside to the outside and the voltage continues to decrease, it is considered a discharge state.

[0069] Other states are judged as battery balancing processes.

[0070] In one or more embodiments, preferably, the real-time temperature monitoring of the battery and prompting of an overheating state specifically includes:

[0071] Collect current battery temperature in real time;

[0072] Obtain historical battery temperature peaks;

[0073] When the current battery temperature reaches the historical maximum temperature or reaches 80% of the preset battery steady-state maximum operating temperature, an overheating prompt is issued.

[0074] According to a second aspect of an embodiment of the present invention, a battery type identification and detection system based on charge and discharge characteristics is provided.

[0075] In one or more embodiments, preferably, the battery type identification and detection system based on charge and discharge characteristics includes:

[0076] Data acquisition module, used to obtain the charge and discharge information of each battery in real time;

[0077] a platform voltage determination module, configured to determine a battery platform based on the charge and discharge information, wherein the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery;

[0078] The slope characteristic analysis module is used to calculate the slope characteristics of the current battery in real time, determine the similarity between the current battery and the preset battery type, and select the battery type with the highest similarity as the matching result;

[0079] Abnormal analysis module, used to extract and display abnormal jump information inside the battery in real time;

[0080] The charge and discharge analysis module is used to analyze the current battery status based on the changes in current and voltage, where the status includes charging, discharging and balancing;

[0081] The overheating analysis module is used to monitor the battery temperature in real time and prompt overheating status.

[0082] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0083] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0084] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0085] In the solution of the present invention, the standardized slope calculation makes the comparison of the charge and discharge slopes of different batteries more scientific and reliable, providing a quantitative basis for judging the battery type.

[0086] In the solution of the present invention, the corresponding rules between current battery characteristics and types are effectively learned and analyzed to improve recognition accuracy and stability.

[0087] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0088] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0090] Figure 1 The present invention is a flowchart of a method for identifying and detecting battery types based on charge and discharge characteristics according to an embodiment of the present invention.

[0091] Figure 2 The present invention is a flowchart of a method for identifying and detecting battery types based on charge and discharge characteristics, for obtaining charge and discharge information of each battery in real time.

[0092] Figure 3 This is a flowchart of a battery type identification and detection method based on charge and discharge characteristics in an embodiment of the present invention, in which a battery platform is determined according to the charge and discharge information, and the battery platform is one of a ternary lithium battery, a lead-acid battery and a lithium iron phosphate battery.

[0093] Figure 4 This is a flowchart of a battery type identification and detection method based on charge and discharge characteristics in an embodiment of the present invention, which calculates the slope characteristics of the current battery in real time, determines the similarity between the current battery and a preset battery type, and selects the battery type with the highest similarity as the matching result.

[0094] Figure 5 The present invention is a flowchart showing the real-time extraction of abnormal jump information inside a battery in a battery type identification and detection method based on charge and discharge characteristics according to an embodiment of the present invention.

[0095] Figure 6 This is a flowchart of a battery type identification and detection method based on charge and discharge characteristics in an embodiment of the present invention, which analyzes the current battery status according to the changes in current and voltage, wherein the status includes charging, discharging and balancing.

[0096] Figure 7The present invention is a flowchart of a method for identifying and detecting battery types based on charge and discharge characteristics, which monitors battery temperature in real time and prompts overheating status.

[0097] Figure 8 This is a structural diagram of a battery type identification and detection system based on charge and discharge characteristics according to an embodiment of the present invention.

[0098] Figure 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0099] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0100] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0101] In the field of battery testing, battery type identification and detection based on charge and discharge characteristics plays an extremely critical role. Currently, various types of batteries are widely used in many fields, from everyday electronic devices to large-scale applications such as electric vehicles and energy storage systems. Different types of batteries, such as lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries, have significantly different charge and discharge characteristics. By analyzing the battery's voltage, current, time, and capacity changes during the charge and discharge process, it is possible to accurately identify the battery type and effectively detect key parameters such as the battery's health status and remaining capacity. This detection method based on charge and discharge characteristics not only provides a basis for the rational use of batteries and avoids equipment damage and performance degradation caused by the incorrect use of battery types, but also plays a core role in battery management systems, optimizing battery charge and discharge strategies, extending battery life, and improving energy utilization efficiency.

[0102] Prior to the technology of the present invention, the existing battery type identification and detection methods based on charge and discharge characteristics mainly made judgments by monitoring the basic parameters of the battery charge and discharge process, such as the curves of voltage and current changing over time. Some methods use simple threshold comparisons to compare the voltage and current values at a specific stage with pre-set standard values to identify the battery type and evaluate the status. However, this method has many difficulties and key points. The difficulty lies in the fact that the charge and discharge curves of different battery types have a certain degree of similarity, especially under some working conditions, and it is very easy to make misjudgments based on simple parameter comparisons. In addition, in actual use, the battery will be affected by various factors such as ambient temperature and charge and discharge rate, resulting in fluctuations in the charge and discharge characteristics, further increasing the difficulty of accurate identification.

[0103] In one embodiment of the present invention, a battery type identification and detection method and system based on charge and discharge characteristics are provided. This solution determines battery type by integrating voltage platform, slope characteristics, and jump characteristics, performing a combined diagnosis. The combination of multiple features enables analysis from different dimensions, significantly improving identification accuracy and enabling more comprehensive and accurate differentiation between ternary lithium, lead-acid, and lithium iron phosphate batteries.

[0104] According to a first aspect of an embodiment of the present invention, a method for identifying and detecting battery types based on charge and discharge characteristics is provided.

[0105] Figure 1 The present invention is a flowchart of a method for identifying and detecting battery types based on charge and discharge characteristics according to an embodiment of the present invention.

[0106] In one or more embodiments, preferably, the battery type identification and detection method based on charge and discharge characteristics includes:

[0107] S101, acquiring charging and discharging information of each battery in real time;

[0108] S102. Determine a battery platform based on the charge and discharge information, where the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery;

[0109] S103, calculating the slope characteristics of the current battery in real time, and determining the similarity between the current battery and a preset battery type, and selecting the battery type with the highest similarity as the matching result;

[0110] S104, extracting abnormal jump information inside the battery in real time and displaying it;

[0111] S105, analyzing the current state of the battery based on changes in current and voltage, where the current state includes charging, discharging, and balancing;

[0112] S106: Monitor the battery temperature in real time and indicate an overheating condition.

[0113] In the embodiment of the present invention, waveform data is first collected, then the platform voltage is determined, and then the slope characteristic analysis and the jump characteristic analysis are completed, and finally the comprehensive characteristic judgment is completed.

[0114] Figure 2 The present invention is a flowchart of a method for identifying and detecting battery types based on charge and discharge characteristics, for obtaining charge and discharge information of each battery in real time.

[0115] like Figure 2 As shown, in one or more embodiments, preferably, the real-time acquisition of charge and discharge information of each battery specifically includes:

[0116] S201, collecting voltage and current signals during battery charging and discharging;

[0117] S202, transmitting real-time data to a data center for recording at a frequency of 10,000 times per second;

[0118] S203: Obtain a curve showing changes in battery voltage and charging current over time.

[0119] In an embodiment of the present invention, the following method is used to collect, transmit, and obtain battery-related data curves. First, high-precision voltage and current sensors are connected to the battery charging and discharging circuit to collect voltage and current signals during the battery charging and discharging process. These sensors convert the collected analog signals into digital signals. Next, a data transmission module is used to transmit real-time data to a data center for recording at a frequency of 10,000 times per second. The data transmission module can use an Ethernet transmission module or a specific wireless transmission module. By configuring the corresponding parameters, it ensures that the data can be stably transmitted to the data center server at the specified frequency. After receiving the data, the data center uses data analysis software to process the data. This software plots the voltage and current data in a time series to ultimately obtain a curve showing the battery voltage and charging current changing over time. For example, a clear and intuitive curve is drawn with time as the horizontal axis and voltage and current as the vertical axis.

[0120] Figure 3 This is a flowchart of a battery type identification and detection method based on charge and discharge characteristics in an embodiment of the present invention, in which a battery platform is determined according to the charge and discharge information, and the battery platform is one of a ternary lithium battery, a lead-acid battery and a lithium iron phosphate battery.

[0121] like Figure 3 As shown, in one or more embodiments, preferably, the battery platform is determined according to the charge and discharge information, and the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery, specifically including:

[0122] S301, obtaining the voltage value of each battery, setting a collection window, each window including the most recent n data points;

[0123] S302, set m continuous windows, the average value μi and standard deviation σi of the data in each window;

[0124] S303, setting σmax to the maximum standard deviation allowed in each window;

[0125] S304, set E to the maximum allowable fluctuation range of the average value;

[0126] S305, determining whether the first calculation formula is satisfied;

[0127] S306, determining whether the second calculation formula is satisfied;

[0128] S307: If both the first calculation formula and the second calculation formula are satisfied, the voltage value at the current moment is considered to be a plateau voltage; otherwise, the determination is continued until a plateau voltage appears.

[0129] S308. If the platform voltage is between 3.6V and 4.2V, it is considered to be a ternary lithium battery charging platform. If the platform voltage is between 2.3V and 2.4V, it is considered to be a lead-acid battery charging platform. If the platform voltage is between 3.2V and 3.4V, it is considered to be a lithium iron phosphate battery charging platform.

[0130] The first calculation formula is:

[0131]

[0132] Among them, σ j is the standard deviation of the jth window, M is the total number of consecutive windows, and j is the window number;

[0133] The second calculation formula is:

[0134]

[0135] Among them, max() is the maximum value selection module, min() is the minimum value selection module, μ M is the average value of the data in the Mth window, and E is the maximum fluctuation range allowed for the average value.

[0136] In an embodiment of the present invention, after completing operations such as voltage and current signal acquisition and real-time temperature monitoring during the battery charge and discharge process, the acquired charge and discharge information is used to determine the battery platform type. Battery platform types include ternary lithium batteries, lead-acid batteries, and lithium iron phosphate batteries. First, the data center continuously acquires the voltage value of each battery. To facilitate analysis of voltage data characteristics, an acquisition window is set. Each window contains the most recent n data points. For example, if n is 10, each window contains the most recent 10 voltage data points. Furthermore, m consecutive windows are set. For example, if m is 5, there are five consecutive windows for subsequent analysis. The mean and standard deviation of the data within each window are calculated. For example, for the first window, the 10 voltage data points are summed and divided by 10 to obtain the mean. The standard deviation for this window is then calculated using the standard deviation calculation formula. The mean and standard deviation for each window are calculated in this manner. A maximum standard deviation is preset for each window. This value can be determined based on battery characteristics and extensive experimental data. For example, a maximum fluctuation range for the mean value is set, assuming a value of 0.03. Next, determine whether the first and second calculation formulas are satisfied. If both formulas are satisfied, the current voltage value is considered to be a platform voltage. Based on the obtained platform voltage, the battery's platform type is determined. If the platform voltage is between 3.6V and 4.2V, the battery is considered to be a ternary lithium battery charging platform; if the platform voltage is between 2.3V and 2.4V, it is considered to be a lead-acid battery charging platform; if the platform voltage is between 3.2V and 3.4V, it is considered to be a lithium iron phosphate battery charging platform. For example, if the calculated platform voltage is 3.8V, since 3.6V < 3.8V < 4.2V, it can be determined that the battery is a ternary lithium battery. Through the above steps, using the collected battery charge and discharge information, through data processing and formula judgment, the battery's platform type can be accurately determined.

[0137] Figure 4 This is a flowchart of a battery type identification and detection method based on charge and discharge characteristics in an embodiment of the present invention, which calculates the slope characteristics of the current battery in real time, determines the similarity between the current battery and a preset battery type, and selects the battery type with the highest similarity as the matching result.

[0138] like Figure 4 As shown, in one or more embodiments, preferably, the real-time calculation of the slope characteristics of the current battery, and the determination of the similarity between the current battery and the preset battery type, and the selection of the battery type with the highest similarity as the matching result, specifically include:

[0139] S401, calculating the linear regression slope using a third calculation formula;

[0140] S402, calculating slope similarity using a fourth calculation formula based on the linear regression slopes of the two data sets;

[0141] S403, using the fifth calculation formula to calculate the slope change rate, when the slope change rate is greater than a preset value, it is determined to be the peak start time, and the maximum value of the data value after the peak start is extracted as the peak highest point;

[0142] S404, calculate the peak height using the sixth calculation formula,

[0143] S405, using the eighth calculation formula to determine the peak similarity between the current battery and the preset battery type;

[0144] S406, calculating the comprehensive quantitative similarity using the ninth calculation formula;

[0145] S407, calculating the battery type with the highest comprehensive similarity to the current battery as a matching result;

[0146] The third calculation formula is:

[0147]

[0148] Where m is the linear regression slope, x i is the time point, y i is the data value, n is the data amount;

[0149] The fourth calculation formula is:

[0150]

[0151] Wherein, S12 is the slope similarity of the data set, m1 and m2 are the linear regression slopes of the data set respectively;

[0152] The fifth calculation formula is:

[0153]

[0154] Among them, △m i is the slope change rate at the i-th data moment, x i is the i-th time point, y i is the i-th data value, x i+1 is the i+1th time point, y i+1 is the i+1th data value;

[0155] The sixth calculation formula is:

[0156] H=max(ypeak-ybase)

[0157] Where H is the peak height, ypeak is the highest point of the peak, and ybase is the data value at the time when the peak starts;

[0158] The seventh calculation formula is:

[0159] T=tend-tstart

[0160] Among them, T is the duration, tend is the moment of the peak's highest point, and tstart is the moment when the peak starts;

[0161] The eighth calculation formula is:

[0162]

[0163] Among them, S F is the peak similarity, H1 and H2 are the peak heights of the current battery and the preset battery type respectively, and T1 and T2 are the durations of the current battery and the preset battery type respectively;

[0164] The ninth calculation formula is:

[0165] S Z =w1×S 12 +w2×S F

[0166] Among them, S Z is the comprehensive similarity, w1 and w2 are the first and second weight coefficients respectively.

[0167] In an embodiment of the present invention, after completing the acquisition of voltage and current signals during the battery charging and discharging process and transmitting the real-time data to a data center for recording, the slope characteristics of the current battery are calculated in real time and their similarity with a preset battery type is determined. First, for the current battery dataset, the linear regression slope is calculated using the third calculation formula, where xi represents the time point, yi represents the data value (here, the data value can be a voltage value or a current value), and n represents the number of data points. For example, if 100 voltage data points are collected over a certain period of time, these data points can be substituted into the above formula to calculate the linear regression slope. Next, the slope similarity between the current battery dataset and another dataset (which can be a dataset corresponding to a preset battery type) is calculated using the fourth calculation formula, where m1 and m2 are the linear regression slopes of the two datasets, respectively. Next, the slope change rate is calculated using the fifth calculation formula, where xi represents the i-th time point, yi represents the i-th data value, x{i + 1} represents the (i + 1)-th time point, and y{i + 1} represents the (i + 1)-th data value. When the calculated slope change rate is greater than a preset value (for example, 0.5, which can be determined based on actual experience and research), the peak initiation moment is determined. Subsequently, the maximum value of the data value after the peak initiation is extracted as the peak peak point. The peak height is then calculated using the sixth calculation formula, where ypeak is the peak peak point and ybase is the data value at the peak initiation moment. The duration is calculated using the seventh calculation formula, where tend is the time of the peak peak. The peak similarity between the current battery and the preset battery type is then calculated using the eighth calculation formula. Finally, the comprehensive quantitative similarity is calculated using the ninth calculation formula, where w1 and w2 are the first and second weight coefficients, respectively (for example, w1 = 0.6 and w2 = 0.4; the weight coefficients should also be determined based on actual conditions and research objectives). This calculation process is repeated for multiple preset battery types, and the resulting comprehensive similarities are compared. The preset battery type with the highest comprehensive similarity is selected as the matching result for the current battery.

[0168] Figure 5 The present invention is a flowchart showing the real-time extraction of abnormal jump information inside a battery in a battery type identification and detection method based on charge and discharge characteristics according to an embodiment of the present invention.

[0169] like Figure 5 As shown, in one or more embodiments, preferably, the real-time extraction and display of abnormal jump information inside the battery specifically includes:

[0170] S501, observe whether there is a voltage jump phenomenon in the voltage-time waveform;

[0171] S502: If the amplitude is the same as or close to the preset jump threshold, it is determined that there is a battery abnormality, and the waveform showing the abnormal jump process is marked.

[0172] In this embodiment of the present invention, to extract abnormal voltage jump information, the data center receives real-time voltage-time waveform data and uses specialized signal analysis software to monitor the waveform in real time. Specifically, the voltage values on the voltage-time waveform are read sequentially at very short intervals (e.g., 0.0001 seconds). A pre-set threshold, assumed to be 0.3 volts (this value can be appropriately set based on the battery's specifications, performance parameters, and extensive experimental data), is used. During the reading process, the current voltage value is compared with the previous voltage value, and the absolute value of the difference is calculated. If this absolute value is equal to or close to the pre-set threshold, for example, within the range of 0.28 volts to 0.32 volts, an abnormal battery voltage jump is detected. Once an abnormal jump is determined, the software immediately marks and displays the waveform of the abnormal jump. There are many ways to mark and display, such as setting the color of the waveform line of the abnormal jump part to a striking red, and adding text annotations at the corresponding position of the waveform to indicate the specific time and approximate amplitude of the abnormal jump; or popping up a small window next to the waveform to display detailed information about the abnormal jump, including the time when the abnormality occurred, the voltage values before and after the jump, etc., so that relevant personnel can quickly and accurately understand the abnormal situation of the battery and take appropriate measures to deal with it in time.

[0173] Figure 6 This is a flowchart of a battery type identification and detection method based on charge and discharge characteristics in an embodiment of the present invention, which analyzes the current battery status according to the changes in current and voltage, wherein the status includes charging, discharging and balancing.

[0174] like Figure 6 As shown, in one or more embodiments, preferably, the current state of the battery is analyzed based on the changes in current and voltage, where the current state includes charging, discharging, and balancing, specifically including:

[0175] S601: Determine that the direction of the battery current is from the outside to the inside and the voltage continues to increase, indicating a charging state;

[0176] S602: Determine that the direction of the battery current is from the inside to the outside and the voltage continues to decrease, indicating a discharge state;

[0177] S603: Determine whether the other status is a battery balancing process.

[0178] In an embodiment of the present invention, battery status analysis relies on collected voltage and current signals during the battery's charge and discharge processes. These signals are transmitted to a data center for recording at a frequency of 10,000 times per second. The data center utilizes high-precision current and voltage sensors to acquire real-time battery current and voltage data, providing a basis for accurate battery status assessment. When the current sensor detects current flowing from the outside to the inside of the battery, the data center's analysis system continuously monitors the voltage data. The system chronologically compares the voltage values at adjacent time points within a set time period (e.g., 10 seconds). If the voltage value at each subsequent time point within this time period is greater than the previous value, and the voltage increase is within a reasonable range (e.g., each increase does not exceed 0.1 volt), the battery is considered to be charging. For example, if, within a 10-second period, voltage values of 3.2V, 3.22V, 3.24V, 3.26V, and so on are recorded sequentially, and the current direction is external to the battery, the battery is considered to be charging. If the current sensor detects current flowing from the battery's interior to the exterior, the analysis system will also continuously monitor the voltage data. Within a set 10-second period, the voltage values at adjacent time points are compared. If the voltage value at each subsequent time point is lower than the previous one, and the voltage drop is within a reasonable range (for example, no more than 0.1 volt per drop), the battery is determined to be discharging. For example, if voltage values of 3.8V, 3.78V, 3.76V, and 3.74V are recorded within 10 seconds, and the current is flowing out of the battery, the battery is determined to be discharging. When the battery's current and voltage changes meet neither the criteria for charging nor the criteria for discharging, the battery is considered to be in the balancing process. For example, if the current direction is unstable, sometimes flowing from the exterior to the interior and sometimes from the interior to the exterior over a period of time; or if the voltage value does not show a continuous increasing or decreasing trend but fluctuates within a small range (for example, the voltage value fluctuates between 3.5V and 3.55V), the battery can be determined to be in the balancing process. Through the above real-time monitoring and analysis of current and voltage changes, the battery's current state can be accurately judged, providing an important basis for scientific management and maintenance of the battery, ensuring safe and stable operation of the battery.

[0179] Figure 7 The present invention is a flowchart of a method for identifying and detecting battery types based on charge and discharge characteristics, which monitors battery temperature in real time and prompts overheating status.

[0180] like Figure 7 As shown, in one or more embodiments, preferably, the real-time temperature monitoring of the battery and prompting of overheating state specifically include:

[0181] S701, collecting the current battery temperature in real time;

[0182] S702, obtaining historical battery temperature peak values;

[0183] S703: When the current battery temperature reaches the historical maximum temperature or reaches 80% of the preset battery steady-state maximum temperature, an overheating prompt is issued.

[0184] In an embodiment of the present invention, after completing the acquisition, transmission, and related data processing of voltage and current signals during the battery charge and discharge process, including slope characteristic calculation, abnormal transition information extraction, and battery status analysis, real-time battery temperature monitoring and overheating indication are also required. While receiving real-time voltage and current data, the data center uses a high-precision temperature sensor to collect the current battery temperature in real time. The temperature sensor must possess fast response and high-precision measurement characteristics to accurately capture subtle changes in battery temperature. To effectively monitor temperature, the system records and stores historical battery temperature data. The system automatically updates the historical data record each time new temperature data is collected. By analyzing historical data, historical battery temperature peaks can be obtained. The system pre-sets a maximum steady-state operating temperature for the battery. This value can be reasonably determined based on the battery's specifications, performance parameters, and extensive experimental data. For example, for a certain battery model, the maximum steady-state operating temperature has been determined to be 60°C after multiple experiments and tests. During real-time monitoring, the system continuously compares the currently collected battery temperature with the historical temperature peak and a preset value of 80% of the battery's maximum steady-state operating temperature. Once the current battery temperature reaches its historical maximum temperature or 80% of the preset maximum steady-state operating temperature (i.e., 60°C x 80% = 48°C), the system will immediately issue an overheating warning. Overheating warnings can be implemented in a variety of ways to ensure that relevant personnel are promptly notified of battery overheating. For example, the system can display a prominent red warning box on the data center monitoring interface, with the text message "Battery overheating, please address it immediately." Simultaneously, the system can trigger an audible alarm with a loud and continuous sound. Furthermore, overheating information can be sent to relevant personnel's mobile phones or email addresses via text message or email, allowing them to receive timely information and take appropriate measures, such as adjusting battery charge and discharge parameters or providing heat dissipation treatment, to ensure safe and stable battery operation.

[0185] According to a second aspect of an embodiment of the present invention, a battery type identification and detection system based on charge and discharge characteristics is provided.

[0186] Figure 8 This is a structural diagram of a battery type identification and detection system based on charge and discharge characteristics according to an embodiment of the present invention.

[0187] In one or more embodiments, preferably, the battery type identification and detection system based on charge and discharge characteristics includes:

[0188] Data acquisition module 801, used to obtain the charge and discharge information of each battery in real time;

[0189] a platform voltage determination module 802 for determining a battery platform based on the charge and discharge information, wherein the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery;

[0190] Slope feature analysis module 803, used to calculate the slope feature of the current battery in real time, determine the similarity between the current battery and the preset battery type, and select the battery type with the highest similarity as the matching result;

[0191] Abnormal analysis module 804, used to extract abnormal jump information inside the battery in real time and display it;

[0192] The charge and discharge analysis module 805 is used to analyze the current state of the battery based on the changes in current and voltage, where the current state includes charging, discharging, and balancing;

[0193] The overheat analysis module 806 is used to monitor the battery temperature in real time and prompt an overheat state.

[0194] In the embodiment of the present invention, a system applicable to different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0195] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0196] According to a fourth aspect of the embodiments of the present invention, an electronic device is provided. Figure 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Figure 9The electronic device shown is a battery type identification and detection device based on charge and discharge characteristics. It includes a general computer hardware structure, including at least a processor 901 and a memory 902. The processor 901 and the memory 902 are connected via a bus 903. The memory 902 is suitable for storing instructions or programs executable by the processor 901. The processor 901 can be a standalone microprocessor or a collection of one or more microprocessors. Thus, by executing the instructions stored in the memory 902, the processor 901 performs the method flow described above in the embodiment of the present invention to process data and control other devices. The bus 903 connects the above-mentioned components together and also connects them to a display controller 904 and a display device, as well as an input / output (I / O) device 905. The I / O device 905 can be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer, or other devices known in the art. Typically, the I / O device 905 is connected to the system via an I / O controller 906.

[0197] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0198] In the solution of the present invention, the standardized slope calculation makes the comparison of the charge and discharge slopes of different batteries more scientific and reliable, providing a quantitative basis for judging the battery type.

[0199] In the solution of the present invention, the corresponding rules between current battery characteristics and types are effectively learned and analyzed to improve recognition accuracy and stability.

[0200] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0201] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0202] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0203] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0204] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A battery type identification and detection method based on charge and discharge characteristics, characterized in that: The method includes: Get the charge and discharge information of each battery in real time; Determining a battery platform based on the charge and discharge information, where the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery; Calculate the slope characteristics of the current battery in real time, determine the similarity between the current battery and the preset battery type, and select the battery type with the highest similarity as the matching result; Extract and display abnormal jump information inside the battery in real time; Analyze the current battery status based on the changes in current and voltage, including charging, discharging, and balancing. Real-time battery temperature monitoring and overheating prompts; The real-time calculation of the slope characteristics of the current battery, the determination of the similarity between the current battery and a preset battery type, and the selection of the battery type with the highest similarity as the matching result specifically include: Calculate the linear regression slope using the third calculation formula; Calculate the slope similarity using the fourth calculation formula according to the linear regression slopes of the two data sets; The slope change rate is calculated using the fifth calculation formula. When the slope change rate is greater than a preset value, it is determined to be the peak start time, and the maximum value of the data value after the peak start is extracted as the peak highest point; Calculate the peak height using the sixth calculation formula, and calculate the duration using the seventh calculation formula; Using the eighth calculation formula to determine the peak similarity between the current battery and the preset battery type; Calculate the comprehensive quantitative similarity using the ninth calculation formula; Calculate the battery type with the highest comprehensive similarity to the current battery as the matching result; The third calculation formula is: ； Where m is the linear regression slope, x i is the time point, y i is the data value, n is the data amount; The fourth calculation formula is: ； Wherein, S12 is the slope similarity of the data set, m1 and m2 are the linear regression slopes of the data set respectively; The fifth calculation formula is: ； Among them, △m i is the slope change rate at the i-th data moment, x i is the i-th time point, y i is the i-th data value, x i+1 is the i+1th time point, y i+1 is the i+1th data value; The sixth calculation formula is: H =max( y peak- y base) Where H is the peak height, y Peak is the highest point of the peak, y base is the data value at the time when the peak starts; The seventh calculation formula is: T = t end- t start Where T is the duration, t End is the moment of the peak's highest point, t start is the peak start time; The eighth calculation formula is: ； Among them, S F is the peak similarity, H1 and H2 are the peak heights of the current battery and the preset battery type respectively, and T1 and T2 are the durations of the current battery and the preset battery type respectively; The ninth calculation formula is: ； Among them, S Z is the comprehensive similarity, w1 and w2 are the first and second weight coefficients respectively.

2. A battery type identification and detection method based on charge and discharge characteristics according to claim 1, characterized in that: The real-time acquisition of charge and discharge information of each battery specifically includes: Collect voltage and current signals during battery charging and discharging; Transmit real-time data to the data center for recording at a frequency of 10,000 times per second; Obtain the curve of battery voltage and charging current changing with time.

3. The battery type identification and detection method based on charge and discharge characteristics according to claim 1, characterized in that: The determining of a battery platform according to the charge and discharge information, wherein the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery, specifically includes: Get the voltage value of each battery; Set the acquisition window, each window includes the latest n data points; Set m consecutive windows; The average value μ of the data in each window i and standard deviation σ i ; Setting σ max is the maximum standard deviation allowed within each window; Set E to the maximum fluctuation range allowed for the average value; Determine whether the first calculation formula is satisfied; Determining whether the second calculation formula is satisfied; If both the first and second calculation formulas are satisfied, the voltage value at the current moment is considered to be a plateau voltage. Otherwise, the determination is continued until a plateau voltage appears. If the platform voltage is between 3.6V and 4.2V, it is considered to be a ternary lithium battery charging platform. If the platform voltage is between 2.3V-2.4V, it is considered to be a lead-acid battery charging platform. If the platform voltage is between 3.2V-3.4V, it is considered to be a lithium iron phosphate battery charging platform. The first calculation formula is: ； in, σj For the j The standard deviation of the window, M is the total number of consecutive windows, j Number the window; The second calculation formula is: ； in, max () is the maximum value selection module, min () is the minimum value selection module, μM is the average value of the data in the Mth window, E The maximum allowed fluctuation range of the average value.

4. The battery type identification and detection method based on charge and discharge characteristics according to claim 1, characterized in that: The real-time extraction and display of abnormal jump information inside the battery specifically includes: Observe whether there is voltage jump in the voltage-time waveform; If the amplitude is the same or similar to the preset jump threshold, it is considered that there is a battery abnormality, and the waveform showing the abnormal jump process is marked.

5. The battery type identification and detection method based on charge and discharge characteristics according to claim 1, characterized in that: The current state of the battery is analyzed based on the changes in current and voltage, where the current state includes charging, discharging, and balancing, specifically including: If the current direction of the battery is from the outside to the inside and the voltage continues to increase, it is considered to be in the charging state; If the battery current direction is from the inside to the outside and the voltage continues to decrease, it is considered a discharge state. Other states are judged as battery balancing processes.

6. The battery type identification and detection method based on charge and discharge characteristics according to claim 1, characterized in that: The real-time battery temperature monitoring and overheating warning are as follows: Collect current battery temperature in real time; Obtain historical battery temperature peaks; When the current battery temperature reaches the historical maximum temperature or reaches 80% of the preset battery steady-state maximum operating temperature, an overheating prompt is issued.

7. A battery type identification and detection system based on charge and discharge characteristics, characterized in that: The system is used to implement the method according to any one of claims 1 to 6, and the system comprises: Data acquisition module, used to obtain the charge and discharge information of each battery in real time; a platform voltage determination module, configured to determine a battery platform based on the charge and discharge information, wherein the battery platform is one of a ternary lithium battery, a lead-acid battery, and a lithium iron phosphate battery; The slope characteristic analysis module is used to calculate the slope characteristics of the current battery in real time, determine the similarity between the current battery and the preset battery type, and select the battery type with the highest similarity as the matching result; Abnormal analysis module, used to extract and display abnormal jump information inside the battery in real time; The charge and discharge analysis module is used to analyze the current battery status based on the changes in current and voltage, where the status includes charging, discharging and balancing; The overheating analysis module is used to monitor the battery temperature in real time and prompt overheating status.

8. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 6 when executed by a processor.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 6.

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