Battery life remote detection system and method based on intelligent analysis of charging data

Abstract

The present invention discloses a remote detection system and method for battery life based on intelligent analysis of charging data, which relates to the technical field of battery life analysis. The data collection of the remote detection system for battery life is realized based on the push data of the charging pile platform, and the battery charging order data pushed by the charging pile platform is received. When the user completes charging at the charging pile, the supplier of the charging pile platform will push the heartbeat data to the remote detection system for battery life. Through the obtained battery charging order data, the data of key information such as voltage, current, power, etc. during the charging process and the change trend are obtained. Before entering the battery life analysis process, the present invention first needs to confirm the validity of the order, ensure that the charging data of each vehicle is accurately classified, accurately judge the battery performance, and realize accurate prediction of the battery life during charging. It can make a more accurate assessment of the battery life status from a macro perspective, thereby optimizing the user experience and effectively extending the battery service life.

Description

Technical Field

[0001] The present invention relates to the technical field of battery life analysis, and in particular to a battery life remote detection system and method based on intelligent analysis of charging data. Background Art

[0002] With the increasing popularity of electric bicycles, the reliability of their charging equipment and the durability of their batteries have become particularly important. Currently, common battery life analysis methods on the market rely on manual operations for testing and maintenance. This is not only inefficient but also prone to omissions and misjudgments, failing to meet the growing needs of users. In addition, battery life predictions are usually based on basic indicators such as the number of charge cycles, usage time, and charging time. These methods often fail to provide accurate information about the actual performance status of the battery, leading to uncertainty and potential safety risks for users during use. Therefore, the development of a more efficient and accurate battery life analysis system has become an urgent problem that the industry needs to solve. Summary of the Invention

[0003] In order to solve the technical problem of analyzing the battery life of electric bicycles, the present invention provides a remote battery life detection system and method based on intelligent analysis of charging data. The following technical solutions are adopted:

[0004] A remote battery life detection method based on intelligent analysis of charging data includes the following steps:

[0005] Step 1: Data collection: The battery life remote detection system receives the battery charging order data pushed by the charging pile platform;

[0006] Step 2: Data validity check: The battery life remote detection system determines whether the collected battery charging order data is valid based on the set charging time threshold and the characteristics of the charging power curve in the historical battery charging order data;

[0007] Step 3: If the battery charging order data is determined to be valid in step 2, the battery charging order data is parsed to identify battery characteristics. Based on the battery characteristic identification results, a pre-stored battery life rule is obtained. The indicators required for the analysis process are obtained according to the battery life rule. The indicators are the effective charging time and the effective charging power range.

[0008] Step 4, calculating the effective charging time: Based on the battery charging order data obtained in step 1 and the effective charging power range obtained in step 3, the charging time within the effective charging power range in all charging heartbeat data is determined in sequence, and the total effective charging time of the battery charging order data is calculated by cumulative addition;

[0009] Step 5, battery life detection, determines the battery life according to the battery effective charging time in the battery life analysis rule in step 3 and the total effective charging time of the battery calculated in step 4.

[0010] Optionally, in step 1, the battery charging order data includes multiple charging heartbeat data based on a time series, and the charging heartbeat data includes performance parameters of the electric bicycle battery during charging, timestamp data of each data push, and device identification information data.

[0011] By adopting this technical solution, the remote battery life monitoring system collects data based on push data from the charging station platform. Leveraging IoT sensing technology, it receives battery charging order data pushed by the charging station platform, which is derived from the charging process of the user's e-bike. This heartbeat data not only records key performance parameters of the e-bike charging process, such as voltage, current, and power, as well as the timestamp of each data push, but also includes user and device identification information, such as user ID, supplier ID, and order number.

[0012] When a user completes charging at a charging station, the charging station platform provider pushes heartbeat data to the remote battery life monitoring system. This data contains a specific flag that clearly indicates the end of the charging process. Based on the user identifier and order information contained in this heartbeat data, the remote battery life monitoring system retrieves all time-series heartbeat data records accumulated since the start of the battery charging order data from the backend database, constructing a complete battery charging order database. This battery charging order data can be used to obtain key information such as voltage, current, and power during the charging process, as well as their changing trends, providing an information foundation for subsequent judgments.

[0013] Before entering the battery life analysis process, the order validity must be confirmed. This is defined as follows: the charging time of the battery charging order data must be greater than a set threshold. If the battery charging order data does not meet this validity criterion, the program will abort and not proceed to the subsequent judgment process. Conversely, if the conditions are met, the system will continue to execute the subsequent battery life judgment logic.

[0014] At the same time, identifying and classifying the data for multiple e-bikes owned by a single user is crucial. This is because the charging data for different vehicles can vary significantly. If these differences are not correctly distinguished, they can lead to confusion and misjudgment in the analysis results. Therefore, by analyzing the characteristics of the charging power curves in the user's historical battery charging order data, the system can effectively distinguish and identify the batteries of multiple e-bikes owned by the user. This step ensures that the charging data for each vehicle is accurately classified, providing an accurate data foundation for each specific order. This classified order data is then fed into subsequent processing steps for detailed analysis to accurately determine battery performance. This approach avoids data confusion caused by users owning multiple vehicles and ensures the accuracy and reliability of battery life analysis.

[0015] Optionally, step 2 includes the following sub-steps:

[0016] Step 21, charging time threshold determination, assuming the battery charging time obtained from the battery charging order data is T total , set the charging time threshold as T m , if T total >T m Then continue to judge;

[0017] Step 22: Analyze whether the characteristics of the battery charging power curve in the battery charging order data match the characteristics of the corresponding battery charging power curve in the historical battery charging order data of the corresponding user. If they match, the battery charging order data is judged to be valid, otherwise it is judged to be invalid.

[0018] Optionally, if the characteristics of the battery charging power curve in the battery charging order data in step 22 do not match the characteristics of the corresponding battery charging power curve in the historical battery charging order data of the corresponding user, and if no match is found in the multiple battery charging order data of the corresponding user, a judgment result that the battery charging order data is invalid is output;

[0019] If there is a battery charging power curve feature match with one of the multiple battery charging order data in the historical battery charging order data of the corresponding user, step 3 uses the corresponding successfully matched battery data to obtain battery life rules.

[0020] By adopting the above technical solution, the specific charging time threshold T m It can be set to 3 hours. When the battery is charged for a long time, total If the time is greater than 3 hours, then proceed to step 22 for curve feature matching judgment;

[0021] The subsequent feature matching of the charging power curve can be achieved using the following specific methods:

[0022] Extract statistical features of the charging power curve, such as mean, maximum, minimum, standard deviation, median, etc. Analyze the shape characteristics of the curve, such as rising slope, falling slope, inflection point, peak-to-valley value, etc.

[0023] Analyze the time-series dynamic characteristics of the power curve, such as the changing trend of the charging rate and the division of charging stages. Calculate the Euclidean distance between the two sets of power curve feature vectors. The smaller the distance, the higher the similarity. Cosine similarity: Measures the directional similarity between the two sets of feature vectors. Values ​​closer to 1 indicate greater similarity.

[0024] It is also possible to train historical data based on machine learning methods using supervised learning algorithms (such as support vector machines, random forests, neural networks, etc.), and then classify new charging curves after building a model to determine whether they match historical data.

[0025] Optionally, in step 4, the method for calculating the total effective charging time is:

[0026] The maximum power during charging is multiplied by the effective charging power percentage to obtain the effective charging power range. The system traverses each charging heartbeat data of the battery charging order data and evaluates whether the power value in the charging heartbeat data falls within the effective charging power range. The duration of the records that meet the conditions will be accumulated to calculate the total effective charging time T of the entire order. effcient .

[0027] Optionally, set the maximum power P during charging max , the maximum effective charging power percentage is a up , the minimum effective charging power percentage is a down , the maximum effective charging power in the effective charging power range is P up , the minimum effective charging power is P down ;

[0028] Then P up =P max ·a up ;P down =P max ·a down ;

[0029] The effective charging power range is P down —P up .

[0030] With the above technical solution, it's important to note that when analyzing the duration within the maximum power range and the abnormal power range, because the power curve is essentially a scatter plot of heartbeat data points, the duration is calculated at the code implementation level by calculating the time difference between adjacent data points and accumulating them. This method of calculating duration is an approximate method, but it has proven effective and practical in real applications, providing reliable data support for battery life assessment.

[0031] Optionally, in step 5, the specific method for determining the battery life is: after completing the traversal of all heartbeat data, compare the total effective charging time with the target effective charging time T in the indicator. target For comparison, if T effcient <T target , then it is judged that the battery life corresponding to the battery charging order data is normal, and the accumulated days and order quantity within the evaluation period are reset; otherwise, it is judged that the battery corresponding to the battery charging order data has a battery life problem, and the accumulated days and order quantity are reset.

[0032] By adopting the above technical solution, the normal life standard of the battery is determined according to the type of battery and the data provided by the manufacturer. This usually includes the expected cycle life (such as 500 times, 1000 times, etc.), the expected calendar life (such as 5 years, 10 years, etc.), and the total effective charging time divided by the number of charges to obtain the average charging time per charge. Estimated number of cycles: Divide the total effective charging time by the average charging time to obtain the approximate number of cycles of the battery. Compare the estimated number of cycles with the expected cycle life of the battery. If the estimated number of cycles is much lower than the expected cycle life, it may indicate that the battery life is abnormal (premature decay). If the estimated number of cycles is close to or slightly lower than the expected cycle life, the battery life may be normal.

[0033] A battery life remote detection system based on intelligent analysis of charging data is used to implement the battery life remote detection method based on intelligent analysis of charging data. The battery life remote detection system includes a charging data acquisition module and a charging data analysis module. The charging data acquisition module is communicatively connected to the charging pile platform to collect battery charging order data pushed by the charging pile platform. The charging data analysis module runs a detection program designed using the battery life remote detection method based on intelligent analysis of charging data, inputs the battery charging order data into the detection program, and the detection program outputs the battery life detection results.

[0034] Optionally, the charging data analysis module is connected to the charging pile platform for communication and the battery life test results output by the interactive detection program.

[0035] By adopting the above technical solution, the charging data acquisition module can directly communicate and interact with the charging pile based on the Internet of Things technology to exchange battery charging order data, and can also directly communicate and interact with the charging pile platform to exchange battery charging order data. The charging data analysis module is mainly implemented based on the server computer. The battery life detection results of the server computer interact with the charging pile platform. The charging pile platform can communicate with the electric bicycle corresponding to the connected battery to feedback the battery life detection results, thereby realizing accurate prediction of battery life during charging.

[0036] In summary, the present invention includes at least one of the following beneficial technical effects:

[0037] The present invention can provide a remote detection system and method for battery life based on intelligent analysis of charging data. The data collection of the remote detection system for battery life is realized based on the push data of the charging pile platform, and the battery charging order data pushed by the charging pile platform is received. When the user completes charging at the charging pile, the supplier of the charging pile platform will push the heartbeat data to the remote detection system for battery life. Through the obtained battery charging order data, the data of key information such as voltage, current, power, etc. during the charging process and the change trend are obtained, thereby providing an information basis for subsequent judgments.

[0038] Before entering the battery life analysis process, order validity must be confirmed to ensure that each vehicle's charging data is accurately categorized, providing an accurate data foundation for each specific order. This categorized order data is then fed into subsequent processing steps for detailed analysis, enabling accurate judgment of battery performance and prediction of battery life during charging. This allows for a more accurate macro-level assessment of battery life status, thereby optimizing the user experience and effectively extending battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of the method for remotely detecting battery life based on intelligent analysis of charging data of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described in detail below with reference to the accompanying drawings.

[0041] The embodiments of the present invention disclose a system and method for remotely detecting battery life based on intelligent analysis of charging data.

[0042] Reference Figure 1 , a remote detection method for battery life based on intelligent analysis of charging data, comprising the following steps:

[0043] Step 1: Data collection: The battery life remote detection system receives the battery charging order data pushed by the charging pile platform;

[0044] Step 2: Data validity check: The battery life remote detection system determines whether the collected battery charging order data is valid based on the set charging time threshold and the characteristics of the charging power curve in the historical battery charging order data;

[0045] Step 3: If the battery charging order data is determined to be valid in step 2, the battery charging order data is parsed to identify battery characteristics. Based on the battery characteristic identification results, a pre-stored battery life rule is obtained. The indicators required for the analysis process are obtained according to the battery life rule. The indicators are the effective charging time and the effective charging power range.

[0046] Step 4, calculating the effective charging time: Based on the battery charging order data obtained in step 1 and the effective charging power range obtained in step 3, the charging time within the effective charging power range in all charging heartbeat data is determined in sequence, and the total effective charging time of the battery charging order data is calculated by cumulative addition;

[0047] Step 5, battery life detection, determines the battery life according to the battery effective charging time in the battery life analysis rule in step 3 and the total effective charging time of the battery calculated in step 4.

[0048] In step 1, the battery charging order data includes multiple charging heartbeat data based on time series, and the charging heartbeat data includes performance parameters of the electric bicycle battery during charging, timestamp data of each data push, and identification information data of the device.

[0049] The remote battery life monitoring system collects data based on push data from the charging station platform. Leveraging IoT sensing technology, it receives battery charging order data from the charging station platform, which is generated during the charging process of the user's e-bike. This heartbeat data not only records key performance parameters during the e-bike charging process, such as voltage, current, and power, as well as the timestamp of each data push, but also includes user and device identification information, such as user ID, supplier ID, and order number.

[0050] When a user completes charging at a charging station, the charging station platform provider pushes heartbeat data to the remote battery life monitoring system. This data contains a specific flag that clearly indicates the end of the charging process. Based on the user identifier and order information contained in this heartbeat data, the remote battery life monitoring system retrieves all time-series heartbeat data records accumulated since the start of the battery charging order data from the backend database, constructing a complete battery charging order database. This battery charging order data can be used to obtain key information such as voltage, current, and power during the charging process, as well as their changing trends, providing an information foundation for subsequent judgments.

[0051] Before entering the battery life analysis process, the order validity must be confirmed. This is defined as follows: the charging time of the battery charging order data must be greater than a set threshold. If the battery charging order data does not meet this validity criterion, the program will abort and not proceed to the subsequent judgment process. Conversely, if the conditions are met, the system will continue to execute the subsequent battery life judgment logic.

[0052] At the same time, identifying and classifying the data for multiple e-bikes owned by a single user is crucial. This is because the charging data for different vehicles can vary significantly. If these differences are not correctly distinguished, they can lead to confusion and misjudgment in the analysis results. Therefore, by analyzing the characteristics of the charging power curves in the user's historical battery charging order data, the system can effectively distinguish and identify the batteries of multiple e-bikes owned by the user. This step ensures that the charging data for each vehicle is accurately classified, providing an accurate data foundation for each specific order. This classified order data is then fed into subsequent processing steps for detailed analysis to accurately determine battery performance. This approach avoids data confusion caused by users owning multiple vehicles and ensures the accuracy and reliability of battery life analysis.

[0053] Step 2 includes the following sub-steps:

[0054] Step 21, charging time threshold determination, assuming the battery charging time obtained from the battery charging order data is T total , set the charging time threshold as T m , if T total >T m Then continue to judge;

[0055] Step 22: Analyze whether the characteristics of the battery charging power curve in the battery charging order data match the characteristics of the corresponding battery charging power curve in the historical battery charging order data of the corresponding user. If they match, the battery charging order data is judged to be valid, otherwise it is judged to be invalid.

[0056] If the characteristics of the battery charging power curve in the battery charging order data in step 22 do not match the characteristics of the corresponding battery charging power curve in the historical battery charging order data of the corresponding user, and if no match is found in the multiple battery charging order data of the corresponding user, a judgment result that the battery charging order data is invalid is output;

[0057] If there is a battery charging power curve feature match with one of the multiple battery charging order data in the historical battery charging order data of the corresponding user, step 3 uses the corresponding successfully matched battery data to obtain battery life rules.

[0058] Specific charging time threshold T mIt can be set to 3 hours. When the battery is charged for a long time, total If the time is greater than 3 hours, then proceed to step 22 for curve feature matching judgment;

[0059] The subsequent feature matching of the charging power curve can be achieved using the following specific methods:

[0060] Extract statistical features of the charging power curve, such as mean, maximum, minimum, standard deviation, median, etc. Analyze the shape characteristics of the curve, such as rising slope, falling slope, inflection point, peak-to-valley value, etc.

[0061] Analyze the time-series dynamic characteristics of the power curve, such as the changing trend of the charging rate and the division of charging stages. Calculate the Euclidean distance between the two sets of power curve feature vectors. The smaller the distance, the higher the similarity. Cosine similarity: Measures the directional similarity between the two sets of feature vectors. Values ​​closer to 1 indicate greater similarity.

[0062] It is also possible to train historical data based on machine learning methods using supervised learning algorithms (such as support vector machines, random forests, neural networks, etc.), and then classify new charging curves after building a model to determine whether they match historical data.

[0063] In step 4, the method for calculating the total effective charging time is:

[0064] The maximum power during charging is multiplied by the effective charging power percentage to obtain the effective charging power range. The system traverses each charging heartbeat data of the battery charging order data and evaluates whether the power value in the charging heartbeat data falls within the effective charging power range. The duration of the records that meet the conditions will be accumulated to calculate the total effective charging time T of the entire order. effcient .

[0065] Assume the maximum power P during charging max , the maximum effective charging power percentage is a up , the minimum effective charging power percentage is a down , the maximum effective charging power in the effective charging power range is P up , the minimum effective charging power is P down ;

[0066] Then P up =P max ·a up ;P down =P max ·a down ;

[0067] The effective charging power range is P down —P up .

[0068] It's important to note that when analyzing duration within the maximum power range and abnormal power range, because the power curve is essentially a scatter plot of heartbeat data points, the code implementation calculates duration by calculating the time differences between adjacent data points and accumulating them. This method of calculating duration is an approximation, but it has proven effective and practical in real-world applications, providing reliable data support for battery life assessment.

[0069] In step 5, the specific method for determining the battery life is: after completing the traversal of all heartbeat data, the total effective charging time is compared with the target effective charging time T in the indicator. target For comparison, if T effcient <T target , then it is judged that the battery life corresponding to the battery charging order data is normal, and the accumulated days and order quantity within the evaluation period are reset; otherwise, it is judged that the battery corresponding to the battery charging order data has a battery life problem, and the accumulated days and order quantity are reset.

[0070] Based on the type of battery and the data provided by the manufacturer, determine the normal life standard of the battery. This usually includes the expected cycle life (such as 500 times, 1000 times, etc.), the expected calendar life (such as 5 years, 10 years, etc.), and the total effective charging time divided by the number of charges to obtain the average charging time per charge. Estimated number of cycles: Divide the total effective charging time by the average charging time to obtain the approximate number of cycles of the battery. Compare the estimated number of cycles with the expected cycle life of the battery. If the estimated number of cycles is much lower than the expected cycle life, it may indicate that the battery life is abnormal (premature degradation). If the estimated number of cycles is close to or slightly lower than the expected cycle life, the battery life may be normal.

[0071] A battery life remote detection system based on intelligent analysis of charging data is used to implement a battery life remote detection method based on intelligent analysis of charging data. The battery life remote detection system includes a charging data acquisition module and a charging data analysis module. The charging data acquisition module is communicated with the charging pile platform to collect battery charging order data pushed by the charging pile platform. The charging data analysis module runs a detection program designed based on the battery life remote detection method based on intelligent analysis of charging data, inputs the battery charging order data into the detection program, and the detection program outputs the battery life detection results.

[0072] The charging data analysis module is connected to the charging pile platform for communication and the battery life test results output by the interactive detection program.

[0073] The charging data acquisition module can be based on the Internet of Things technology to directly communicate and interact with the charging pile to exchange battery charging order data, and can also directly communicate and interact with the charging pile platform to exchange battery charging order data. The charging data analysis module is mainly implemented based on the server computer. The battery life detection results of the server computer interact with the charging pile platform. The charging pile platform can communicate with the electric bicycle corresponding to the connected battery to feedback the battery life detection results, so as to realize accurate prediction of battery life during charging.

[0074] The following describes the principles of a remote battery life detection system and method based on intelligent analysis of charging data based on specific embodiments:

[0075] When a customer's electric bicycle is connected to a charging station, the charging station platform provider pushes heartbeat data to the charging data acquisition module. This data contains a specific flag indicating that the charging process has ended. Based on the user identifier and order information contained in this heartbeat data, the server computer retrieves all time-series heartbeat data records accumulated since the start of the battery charging order data from the backend database, constructing a complete battery charging order data set. This data is then used to obtain key information such as voltage, current, and power during the charging process, as well as their changing trends.

[0076] First, determine the validity of the order and the charging time threshold. Suppose the battery charging time obtained from the battery charging order data is T total =5 hours, and the charging time threshold is T m = 3 hours, T total >T m Continue to judge;

[0077] Analyze whether the characteristics of the battery charging power curve in the battery charging order data match the characteristics of the corresponding battery charging power curve in the corresponding user's historical battery charging order data. Based on machine learning methods, use a supervised learning algorithm to train the historical data. After establishing a model, classify the new charging curve. If the similarity with the battery charging power curve of the electric bicycle with the corresponding number in the historical data is greater than a set value, it is determined to match the historical data, and thus the order is determined to be valid;

[0078] The process for calculating the total effective charging time is as follows:

[0079] The maximum power during charging is multiplied by the effective charging power percentage to obtain the effective charging power range. The system traverses each charging heartbeat data of the battery charging order data and evaluates whether the power value in the charging heartbeat data falls within the effective charging power range. The duration of the records that meet the conditions will be accumulated to calculate the total effective charging time T of the entire order. effcient =1050 hours.

[0080] The recommended life of the battery of the corresponding numbered electric bicycle is estimated to be 1000 cycles (that is, 1000 full charges). The recommended time for each full charge is 6-8 hours. Taking 7 hours, the estimated target effective charging time T is target =7000 hours, T effcient <T target , it is judged that the battery life of the electric bicycle corresponding to the current battery charging order data is normal.

[0081] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A remote battery life detection method based on intelligent analysis of charging data, characterized by: The following steps are involved: Step 1: Data collection: The battery life remote detection system receives the battery charging order data pushed by the charging pile platform; Step 2: Data validity check: The battery life remote detection system determines whether the collected battery charging order data is valid based on the set charging time threshold and the characteristics of the charging power curve in the historical battery charging order data; Step 3: If the battery charging order data is determined to be valid in step 2, the battery charging order data is parsed to identify battery characteristics. Based on the battery characteristic identification results, a pre-stored battery life rule is obtained. The indicators required for the analysis process are obtained according to the battery life rule. The indicators are the effective charging time and the effective charging power range. Step 4, calculating the effective charging time: Based on the battery charging order data obtained in step 1 and the effective charging power range obtained in step 3, the charging time within the effective charging power range in all charging heartbeat data is determined in sequence, and the total effective charging time of the battery charging order data is calculated by cumulative addition; Step 5, battery life detection, based on the battery effective charging time in the battery life analysis rule in step 3 and the total effective charging time of the battery calculated in step 4, the battery life is determined; In step 4, the method for calculating the total effective charging time is: The maximum power during charging is multiplied by the effective charging power percentage to obtain the effective charging power range. The system traverses each charging heartbeat data of the battery charging order data and evaluates whether the power value in the charging heartbeat data falls within the effective charging power range. The duration of the records that meet the conditions will be accumulated to calculate the total effective charging time T of the entire order. effcient .

2. The method for remotely detecting battery life based on intelligent analysis of charging data according to claim 1, characterized in that: In step 1, the battery charging order data includes multiple charging heartbeat data based on time series, and the charging heartbeat data includes performance parameters of the electric bicycle battery during charging, timestamp data of each data push, and identification information data of the device.

3. The method for remotely detecting battery life based on intelligent analysis of charging data according to claim 2, characterized in that: Step 2 includes the following sub-steps: Step 21, charging time threshold determination, assuming the battery charging time obtained from the battery charging order data is T total , set the charging time threshold as T m , if T total >T m Then continue to judge; Step 22: Analyze whether the characteristics of the battery charging power curve in the battery charging order data match the characteristics of the corresponding battery charging power curve in the historical battery charging order data of the corresponding user. If they match, the battery charging order data is judged to be valid, otherwise it is judged to be invalid.

4. The method for remotely detecting battery life based on intelligent analysis of charging data according to claim 3, characterized in that: If the characteristics of the battery charging power curve in the battery charging order data in step 22 do not match the characteristics of the corresponding battery charging power curve in the historical battery charging order data of the corresponding user, and if no match is found in the multiple battery charging order data of the corresponding user, a judgment result that the battery charging order data is invalid is output; If there is a battery charging power curve feature match with one of the multiple battery charging order data in the historical battery charging order data of the corresponding user, step 3 uses the corresponding successfully matched battery data to obtain battery life rules.

5. The method for remotely detecting battery life based on intelligent analysis of charging data according to claim 4, characterized in that: Assume the maximum power P during charging max , the maximum effective charging power percentage is a up , the minimum effective charging power percentage is a down , the maximum effective charging power in the effective charging power range is P up , the minimum effective charging power is P down ; Then P up =P max ·a up ;P down =P max ·a down ; The effective charging power range is P down —P up .

6. The method for remotely detecting battery life based on intelligent analysis of charging data according to claim 5, characterized in that: In step 5, the specific method for determining the battery life is: after completing the traversal of all heartbeat data, the total effective charging time is compared with the target effective charging time T in the indicator. target For comparison, if T effcient <T target , then the battery life corresponding to the battery charging order data is judged to be normal, and the accumulated days and order quantity within the evaluation period are reset; Otherwise, it is determined that the battery corresponding to the battery charging order data has a battery life problem, and the number of days and order quantities are accumulated.

7. A remote battery life detection system based on intelligent analysis of charging data, characterized by: For implementing the remote detection method of battery life based on intelligent analysis of charging data as described in claim 6, the remote detection system of battery life includes a charging data acquisition module and a charging data analysis module, the charging data acquisition module is communicatively connected to the charging pile platform, and collects battery charging order data pushed by the charging pile platform, the charging data analysis module runs a detection program designed using the remote detection method of battery life based on intelligent analysis of charging data as described in claim 6, inputs the battery charging order data into the detection program, and the detection program outputs the battery life detection result.

8. The battery life remote detection system based on intelligent analysis of charging data according to claim 7, characterized in that: The charging data analysis module is connected to the charging pile platform for communication and the battery life test results output by the interactive detection program.

Images