A method and system for monitoring the health of an electric monorail transport vehicle battery

Abstract

The present invention discloses a method and system for monitoring the battery health of an electric monorail transport vehicle, which relates to the technical field of battery health monitoring. The method includes the following steps: obtaining the first basic data of the first battery and the second basic data of the second battery; analyzing the health state of the second battery based on the first basic data and the second basic data; the life prediction model performs life prediction on the second battery according to the second basic data and the health state of the second battery, and determines whether to replace the second battery according to the prediction result. Through the method and system provided by the present invention, the health state of the second battery can be timely understood, and the accuracy of battery health monitoring for the electric monorail transport vehicle can be improved; furthermore, a prompt for battery replacement can be effectively given at a future time node, and at the same time, the battery can be timely adjusted according to the battery health state of the electric monorail transport vehicle, reducing potential safety hazards.

Description

Technical Field

[0001] The present invention relates to the technical field of battery health monitoring, and particularly to a method and system for monitoring the battery health of an electric monorail transport vehicle. Background Art

[0002] Battery health monitoring technology refers to a series of methods and systems for evaluating and predicting the state of a battery. These technologies generally involve real-time monitoring of parameters such as the voltage, current, and temperature of the battery, as well as the processing and analysis of data in order to evaluate the health status, remaining life, and performance of the battery. Battery health monitoring technology is crucial for extending the service life of the battery, improving the efficiency of energy management, and preventing potential safety risks.

[0003] Currently, the prior art proposes a method and system for battery health monitoring and early warning. Based on the voltage, current, and temperature data commonly collected by battery energy storage products, this method can perform battery health detection and risk early warning, reducing the cost requirements for hardware upgrade and replacement; performing risk analysis and early warning of battery health through multi-dimensional data is conducive to improving the sensitivity of early warning; and realizing hierarchical early warning and operation restrictions of battery health risks based on the number of overlimit times, which can ensure the operation efficiency of the user's energy storage battery to the greatest extent while evaluating the battery risk.

[0004] However, when monitoring the battery health of an existing electric monorail transport vehicle, there are still other problems regarding the issue of the discharge time length for the battery capacity. For example: simply by collecting voltage, current, and temperature data, the discharge state of the battery cannot be intuitively monitored, and the conventional monitoring of the battery discharge state is only based on real-time prediction of the battery discharge time, lacking the ability to predict the battery health state, and the obtained data has non-continuous state data, which will interfere with the battery health monitoring data and cannot timely adjust the battery according to the battery health state of the electric monorail transport vehicle, resulting in a relatively large potential safety hazard. Summary of the Invention

[0005] In order to overcome the problem that the existing battery health monitoring technology cannot effectively detect and predict the health state of the battery, thus unable to timely adjust the battery according to the battery health state of the electric monorail transport vehicle, resulting in a relatively large potential safety hazard, the present invention proposes a method and system for monitoring the battery health of an electric monorail transport vehicle. This method can effectively detect and predict the health state of the battery, and then timely adjust the battery according to the battery health state of the electric monorail transport vehicle, reducing potential safety hazards.

[0006] To achieve the object of the present invention, the present invention is implemented by adopting the following technical solutions:

[0007] A method for monitoring the health of a battery of an electric monorail transporter, the method comprising the following steps:

[0008] Obtain the first basic data of the first battery and the second basic data of the second battery;

[0009] Analyze the health status of the second battery based on the first basic data and the second basic data;

[0010] The life prediction model predicts the life of the second battery according to the second basic data and the health status of the second battery, and determines whether to replace the second battery according to the prediction result;

[0011] Wherein, the first battery represents an experimental battery for discharge time testing, and the second battery represents a battery in the use state of the electric monorail transporter.

[0012] In the above technical solution, by analyzing the first basic data and the second basic data, the health status of the second battery can be understood in a timely manner, and the accuracy of the battery health monitoring of the electric monorail transporter can be improved; the life prediction model predicts the life of the second battery according to the second basic data and the health status of the second battery, which can effectively give a prompt for battery replacement at future time nodes, and can also adjust the battery in a timely manner according to the health status of the battery of the electric monorail transporter, reducing potential safety hazards.

[0013] Further, the process of analyzing the health status of the second battery based on the first basic data and the second basic data includes:

[0014] Construct a static model of the first battery based on the first basic data, and construct a dynamic model of the second battery based on the second basic data;

[0015] Using the static model as the underlying data and the dynamic model as the overlay data, analyze the battery health status according to the change range between the overlay data and the underlying data;

[0016] Wherein, both the first basic data and the second basic data include the discharge time data of the corresponding battery.

[0017] Further, the process of constructing a static model of the first battery based on the first basic data includes:

[0018] Taking the battery capacity AQ of the first battery as the ordinate and the discharge time AT of the first battery as the abscissa, establish a plane coordinate system to obtain the static model of the first battery;

[0019] The process of constructing a dynamic model of the second battery based on the second basic data includes:

[0020] Taking the battery capacity BQ of the second battery as the ordinate and the discharge time BT of the second battery as the abscissa, establish a plane coordinate system to obtain the dynamic model of the second battery.

[0021] Further, the process of analyzing the battery health state according to the change ranges of the coverage data and the underlying data includes:

[0022] Mark any point in the static model as (x, y), then mark any point in the dynamic model as (Δx, Δy), and then fuse the coordinate systems of the first battery and the second battery to obtain a new coordinate system with the discharge time Tj as the abscissa and the battery capacity Qj as the ordinate;

[0023] Taking the coordinate system of the first battery as the reference value, at the moment Tk, taking the battery capacity Qk of the first battery 1 as the central value, set the threshold range YQ, and the expression is:

[0024]

[0025] When the battery capacity Qk of the second battery 2 belongs to at this time, the battery is in a healthy state;

[0026] When the battery capacity Qk of the second battery 2 does not belong to at this time, analyze whether the state of the second battery is healthy according to the value exceeding the threshold range YQ in the second battery;

[0027] Among them, represents the lower offset value, represents the upper offset value.

[0028] Further, the process of analyzing whether the state of the second battery is healthy according to the value exceeding the threshold range YQ in the second battery includes:

[0029] Record the value exceeding the threshold range YQ in the second battery as Qs, and the corresponding time as ST;

[0030] Starting from Qs as the starting point and ST as the starting moment, extend to both sides to obtain the abscissas on both sides of the time ST as ST + βT, ST + 2βT,..., ST + nβT and ST - βT, ST - 2βT,..., ST - nβT in turn;

[0031] Record the ordinate values corresponding to the abscissas on both sides of the time ST as αQ, and check whether αQ is a continuously decreasing value. If αQ is a continuously decreasing value, the second battery is in a non - healthy state; if αQ is a non - continuously decreasing value, the second battery is in a healthy state.

[0032] In the above technical solution, by establishing a discharge model of the standard battery, the health state of the battery can be analyzed in a timely manner using the range of data changes, and then the battery can be adjusted in a timely manner according to the health state of the battery of the electric monorail transport vehicle, reducing potential safety hazards.

[0033] Further, in the process of analyzing the health state of the second battery according to the threshold range YQ, the initial discharge test temperature of the first battery is marked as XT, and the final discharge test temperature of the first battery is marked as ZT, satisfying ∣XT - ZT∣≤L;

[0034] Among them, L is the set maximum temperature difference.

[0035] Further, the process of the life prediction model predicting the life of the second battery according to the second basic data includes:

[0036] Eliminate the invalid data in the second basic data, set the regression model to learn the change range of the second basic data, and obtain a life prediction model that masters the change range of the second basic data;

[0037] The life prediction model predicts the effective service life of the second battery in the healthy state according to the change range of the second basic data.

[0038] Further, the process of predicting the effective service life of the second battery in the healthy state includes:

[0039] Calculate the change curvature Δk j at time T j , and the expression is:

[0040] Δk j = Δx / Δy;

[0041] According to the change curvature Δk j , calculate the change curvature Δk j+1 at the next moment T j+1 , and the expression is:

[0042] S = (Δk j - Δk j+1 ) / (T j+1 - T j );

[0043] S calculates the value of Δk j+2 within the next time T j+2 . When the value of the actual Δk j+2 is different from the predicted value of Δk j+2 , at this time, when calculating the value of Δk j+3 within the next time T j+3 , calculate according to two time periods T j and T j+1Calculate the average value of the S value for prediction.

[0044] In the above technical solution, the service life prediction model predicts the life of the second battery based on the second basic data and the health state of the second battery, which can effectively prompt battery replacement at future time nodes, and can also timely adjust the battery according to the health state of the battery of the electric monorail transport vehicle, reducing potential safety hazards.

[0045] An electric monorail transport vehicle battery health monitoring system, the system comprising:

[0046] A data acquisition unit for acquiring first basic data of a first battery and second basic data of a second battery;

[0047] A battery state analysis unit for analyzing the health state of the second battery based on the first basic data and the second basic data;

[0048] A life analysis unit that uses a life prediction model to predict the life of the second battery according to the second basic data and the health state of the second battery, and determines whether to replace the second battery according to the prediction result;

[0049] Wherein, the first battery represents an experimental battery for discharge time testing, and the second battery represents a battery in the use state of the electric monorail transport vehicle.

[0050] An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, wherein when the processor executes the computer program, the steps of an electric monorail transport vehicle battery health monitoring method are implemented.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] The present invention provides an electric monorail transport vehicle battery health monitoring method and system. By analyzing the acquired first basic data and second basic data, the health state of the second battery can be timely understood, and the accuracy of electric monorail transport vehicle battery health monitoring can be improved; the service life prediction model predicts the life of the second battery according to the second basic data and the health state of the second battery, which can effectively prompt battery replacement at future time nodes, and can also timely adjust the battery according to the health state of the battery of the electric monorail transport vehicle, reducing potential safety hazards. Description of the Drawings

[0053] Figure 1 It is a step flow chart of an electric monorail transport vehicle battery health monitoring method provided by an embodiment of the present application;

[0054] Figure 2Schematic diagram of a battery health monitoring system for an electric monorail transport vehicle provided by an embodiment of the present application;

[0055] Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0056] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0058] Embodiment 1:

[0059] This embodiment provides a method for monitoring the health of a battery of an electric monorail transport vehicle. Refer to Figure 1 , the method includes the following steps:

[0060] S1: Obtain the first basic data of the first battery and the second basic data of the second battery;

[0061] S2: Analyze the health status of the second battery based on the first basic data and the second basic data;

[0062] S3: The life prediction model predicts the life of the second battery according to the second basic data and the health status of the second battery, and determines whether to replace the second battery according to the prediction result;

[0063] Wherein, the first battery represents an experimental battery for discharge time test, and the second battery represents a battery in the use state of the electric monorail transport vehicle.

[0064] In step S2, the process of analyzing the health status of the second battery based on the first basic data and the second basic data includes:

[0065] S21: Construct a static model of the first battery based on the first basic data, and construct a dynamic model of the second battery based on the second basic data;

[0066] S22: Using the static model as the underlying data and the dynamic model as the overlaid data, analyze the battery health state according to the variation ranges of the overlaid data and the underlying data;

[0067] Among them, both the first basic data and the second basic data include the discharge time data of the corresponding battery.

[0068] In step S21, the process of constructing the static model of the first battery based on the first basic data includes:

[0069] Taking the battery capacity AQ of the first battery as the ordinate and the discharge time AT of the first battery as the abscissa, establish a plane coordinate system to obtain the static model of the first battery;

[0070] The process of constructing the dynamic model of the second battery based on the second basic data includes:

[0071] Taking the battery capacity BQ of the second battery as the ordinate and the discharge time BT of the second battery as the abscissa, establish a plane coordinate system to obtain the dynamic model of the second battery.

[0072] In step S22, the process of analyzing the battery health state according to the variation ranges of the overlaid data and the underlying data includes:

[0073] Mark any point in the static model as (x, y), then mark any point in the dynamic model as (Δx, Δy), and then fuse the coordinate systems of the first battery and the second battery to obtain a new coordinate system with the discharge time Tj as the abscissa and the battery capacity Qj as the ordinate;

[0074] Taking the coordinate system of the first battery as the reference value, at the moment Tk, taking the battery capacity Qk of the first battery 1 as the central value, set the threshold range YQ, and the expression is:

[0075]

[0076] When the battery capacity Qk of the second battery 2 belongs to at this time, the battery is in a healthy state;

[0077] When the battery capacity Qk of the second battery 2 does not belong to analyze whether the state of the second battery is healthy according to the value of the second battery that exceeds the threshold range YQ;

[0078] Among them, represents the lower offset value, represents the upper offset value.

[0079] Further, the process of analyzing whether the state of the second battery is healthy according to the value of the second battery exceeding the threshold range YQ includes:

[0080] The value of the second battery that exceeds the threshold range YQ is recorded as Qs, and the corresponding time is ST;

[0081] Taking Qs as the starting point and ST as the starting time, extending to both sides, the horizontal coordinates on both sides of time ST are ST+βT, ST+2βT, ..., ST+nβT and ST-βT, ST-2βT, ..., ST-nβT;

[0082] The ordinate values ​​corresponding to the abscissas on both sides of time ST are recorded as αQ, and whether αQ is a continuously decreasing value is checked. If αQ is a continuously decreasing value, the second battery is in an unhealthy state; if αQ is a discontinuously decreasing value, the second battery is in a healthy state.

[0083] It is understandable that by establishing a discharge model for a standard battery and utilizing the range of data changes, the battery health status can be analyzed in a timely manner, and then the battery can be adjusted in a timely manner according to the battery health status of the electric monorail transport vehicle to reduce safety hazards.

[0084] As a preferred embodiment, in step S2, in the process of analyzing the health status of the second battery according to the threshold range YQ, the initial discharge test temperature of the first battery is marked as XT, and the final discharge test temperature of the first battery is marked as ZT, satisfying |XT-ZT|≤L;

[0085] Wherein, L is the set maximum temperature difference.

[0086] In step S3, the process of the life prediction model predicting the life of the second battery according to the second basic data includes:

[0087] S31: Eliminate invalid data in the second basic data, set a regression model to learn the variation range of the second basic data, and obtain a life prediction model that grasps the variation range of the second basic data;

[0088] S32: The life prediction model predicts the effective service life of the second battery in the healthy state according to the variation range of the second basic data.

[0089] In step S32, the process of predicting the effective service life of the second battery in the healthy state includes:

[0090] Calculate T j The curvature of change at time Δk j , the expression is:

[0091] Δk j =Δx / Δy;

[0092] According to the change curvature Δk j , calculate the change curvature Δk j+1 at the next moment T j+1 . The expression is:

[0093] S = (Δk j - Δk j+1 ) / (T j+1 - T j );

[0094] S calculates the value of Δk j+2 within the next time T j+2 . When the value of the actual Δk j+2 is different from the predicted value of Δk j+2 , at this time, when calculating the value of Δk j+3 within the next time T j+3 , according to the average value of the S values calculated for the two periods of time T j and T j+1 for prediction.

[0095] Exemplarily, P1: Within the time T j , calculate the change curvature Δk j at this time according to (Δx, Δy). According to Δk j = Δx / Δy, obtain Δk j . Then calculate Δk j+1 within the next time T j+1 . According to S = (Δk j - Δk j+1 ) / (T j+1 - T j ), calculate the value of Δk j+2 within the next time T j+2 according to S;

[0096] P2: When the value of the actual Δk j+2 is different from the predicted value of Δk j+2 , at this time, when calculating the value of Δk j+3 within the next time T j+3 , according to the average value of the S values calculated for the two periods of time T j and T j+1 for prediction.

[0097] When predicting the effective service life of the second battery in P1, it is only applicable to the second battery analyzed to be in a healthy state. The effective service life of the second battery in a non - healthy state is not applicable to P1 - P2.

[0098] It can be understood that the service life prediction model predicts the life of the second battery based on the second basic data and the health state of the second battery, which can effectively prompt battery replacement at future time nodes, and can also timely adjust the battery according to the health state of the battery of the electric monorail transport vehicle, reducing potential safety hazards.

[0099] In this embodiment, by analyzing the first basic data and the second basic data, the health state of the second battery can be timely understood, and the accuracy of the health monitoring of the battery of the electric monorail transport vehicle can be improved; the service life prediction model predicts the life of the second battery according to the second basic data and the health state of the second battery, which can effectively prompt battery replacement at future time nodes, and can also timely adjust the battery according to the health state of the battery of the electric monorail transport vehicle, reducing potential safety hazards.

[0100] Embodiment 2:

[0101] This embodiment provides a battery health monitoring system for an electric monorail transport vehicle. Refer to Figure 2 , the system includes:

[0102] A data acquisition unit, configured to acquire the first basic data of the first battery and the second basic data of the second battery;

[0103] A battery state analysis unit, configured to analyze the health state of the second battery based on the first basic data and the second basic data;

[0104] A life analysis unit, using a life prediction model to predict the life of the second battery according to the second basic data and the health state of the second battery, and determining whether to replace the second battery according to the prediction result;

[0105] Wherein, the first battery represents an experimental battery for discharge time testing, and the second battery represents a battery in the use state of the electric monorail transport vehicle.

[0106] The system further includes a model construction unit, configured to construct a static model of the first battery based on the first basic data, construct a dynamic model of the second battery based on the second basic data, and construct a life prediction model.

[0107] The system further includes a data storage unit, configured to store the data generated by the data acquisition unit, the battery state analysis unit, the life analysis unit, and the model construction unit.

[0108] Embodiment 3:

[0109] This embodiment provides an electronic device 1000. Refer to Figure 3, including a memory 1010, a processor 1020, and a computer program stored on the memory 1010 and running on the processor 1020. When the processor 1020 executes the computer program, the steps of an electric monorail transport vehicle battery health monitoring method are implemented.

[0110] The processor 1020 can be a Central Processing Unit (CPU), or can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0111] The memory 1010 can include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, the ROM can store static data or instructions required by the processor 1020 or other modules of the computer. The permanent storage device can be a readable and writable storage device. The permanent storage device can be a non-volatile storage device that does not lose the stored instructions and data even when the computer is powered off. In some embodiments, the permanent storage device uses a mass storage device (such as a magnetic or optical disk, flash memory) as the permanent storage device. In some other embodiments, the permanent storage device can be a removable storage device (such as a floppy disk, optical drive). The system memory can be a readable and writable storage device or a volatile readable and writable storage device, such as dynamic random access memory. The system memory can store some or all of the instructions and data required by the processor during operation. In addition, the memory 1010 can include any combination of computer-readable storage media, including various types of semiconductor storage chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and magnetic disks and / or optical disks can also be used. In some embodiments, the memory 1010 can include a removable storage device that is readable and / or writable, such as a compact disc (CD), read-only digital versatile disc (such as DVD-ROM, dual-layer DVD-ROM), read-only Blu-ray disc, super density disc, flash memory card (such as SD card, min SD card, Micro-SD card, etc.), magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and instantaneous electronic signals transmitted wirelessly or wired.

[0112] Executable code is stored in the memory 1010, and when the executable code is processed by the processor 1020, it can cause the processor 1020 to execute some or all of the methods described above.

[0113] The solutions of the present application have been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. Those skilled in the art should also be aware that the actions and modules involved in the specification are not necessarily essential to the present application. In addition, it can be understood that the steps in the method embodiments of the present application can be adjusted, combined, and deleted according to actual needs, and the modules in the device embodiments of the present application can be combined, divided, and deleted according to actual needs.

[0114] In addition, the method according to the present application can also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0115] Alternatively, the present application can also be implemented as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium), on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or an electronic device, a server, etc.), it causes the processor to execute some or all of the steps of the above method according to the present application.

[0116] Those skilled in the art will also understand that the various exemplary logical blocks, modules, circuits, and algorithm steps described in connection with the applications herein can be implemented as electronic hardware, computer software, or a combination of both.

[0117] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems and methods according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0118] The above are only embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A battery health monitoring method for an electric monorail transport vehicle, characterized in that: The method comprises the following steps: Acquire first basic data of the first battery and second basic data of the second battery; Analyzing a health status of the second battery based on the first basic data and the second basic data; constructing a static model of a first battery based on the first basic data, and constructing a dynamic model of a second battery based on the second basic data; A plane coordinate system is established with the battery capacity AQ of the first battery as the ordinate and the discharge time AT of the first battery as the abscissa, thereby obtaining a static model of the first battery; With the battery capacity BQ of the second battery as the ordinate and the discharge time BT of the second battery as the abscissa, a plane coordinate system is established to obtain a dynamic model of the second battery; Taking the static model as underlying data and the dynamic model as overlay data, analyzing the battery health status according to the variation range of the overlay data and the underlying data; The process includes: Mark any point in the static model as (x, y), then mark any point in the dynamic model as (Δx, Δy), and then merge the coordinate systems of the first battery and the second battery to obtain a new coordinate system with the discharge time Tj as the horizontal coordinate and the battery capacity Qj as the vertical coordinate; The coordinate system of the first battery is used as a reference value. At time Tk, the battery capacity Qk1 of the first battery is used as the center value to set the threshold range. The expression is: When the battery capacity Qk2 of the second battery is When , the battery is in a healthy state; When the battery capacity Qk2 of the second battery does not belong to , analyzing whether the state of the second battery is healthy according to the value of the second battery that exceeds the threshold range; in, Indicates the lower offset value. Indicates the upper offset value; The process of analyzing whether the state of the second battery is healthy according to the value exceeding the threshold range in the second battery includes: The value exceeding the threshold range in the second battery is recorded as Qs, and the corresponding time is ST; Taking Qs as the starting point and ST as the starting time, extending to both sides, the horizontal coordinates on both sides of time ST are ST+βT, ST+2βT, ..., ST+nβT and ST-βT, ST-2βT, ..., ST-nβT; The ordinate values ​​corresponding to the abscissas on both sides of the time ST are recorded as αQ, and whether αQ is a continuously decreasing value is checked. If αQ is a continuously decreasing value, the second battery is in an unhealthy state; if αQ is a discontinuously decreasing value, the second battery is in a healthy state; The life prediction model predicts the life of the second battery according to the second basic data and the health status of the second battery, and determines whether to replace the second battery according to the prediction result; The first battery represents an experimental battery for a discharge time test, and the second battery represents a battery in use of an electric monorail transport vehicle.

2. The method for monitoring battery health of an electric monorail transport vehicle according to claim 1, characterized in that: In the process of analyzing the health status of the second battery according to the threshold range, the initial discharge test temperature of the first battery is marked as XT, and the final discharge test temperature of the first battery is marked as ZT, satisfying |XT-ZT|≤L; Wherein, L is the set maximum temperature difference.

3. The method for monitoring battery health of an electric monorail transport vehicle according to claim 1, characterized in that: The process of the life prediction model predicting the life of the second battery according to the second basic data includes: Invalid data in the second basic data are eliminated, and a regression model is set to learn the variation range of the second basic data to obtain a life prediction model that grasps the variation range of the second basic data; The life prediction model predicts the effective service life of the second battery in a healthy state according to the variation range of the second basic data.

4. The method for monitoring battery health of an electric monorail transport vehicle according to claim 3, characterized in that: The process of predicting the effective service life of the second battery in a healthy state includes: Calculate T j The curvature of change at time Δk j , the expression is: Δk j =Δx / Δy; According to the change of curvature Δk j , calculate the next moment T j+1 The change of curvature Δk j+1 , the expression is: S=(Δk j -Δk j+1 ) / (T j+1 -T j ); According to S=(Δk j -Δk j+1 ) / (T j+1 -T j ), use S to calculate the next time T j+2 Internal Δk j+2 When the actual Δk j+2 The numerical and predicted Δk j+2 When the value of is different, the next time T j+3 Internal calculation Δk j+3 When the value is, according to the two time periods T j and T j+1 Calculate the average of the S values ​​for prediction.

5. A battery health monitoring system for an electric monorail transport vehicle, the system being used to implement the function of the method as claimed in any one of claims 1 to 4, characterized in that: The system comprises: A data acquisition unit, used to acquire first basic data of the first battery and second basic data of the second battery; a battery status analysis unit, configured to analyze a health status of the second battery based on the first basic data and the second basic data; a life analysis unit, using a life prediction model to predict the life of the second battery according to the second basic data and the health state of the second battery, and determining whether to replace the second battery according to the prediction result; The first battery represents an experimental battery for a discharge time test, and the second battery represents a battery in use of an electric monorail transport vehicle.

6. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

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