Electric bicycle health status assessment method, system, device and storage medium
By adopting empirical models and capacity calibration methods in electric bicycles, combining the user's habitual charging depth and nominal capacity, the accuracy of battery health status monitoring is solved, and accurate estimation and effective management of battery health status are achieved.
Patent Information
- Application Number
- CN202510784120.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-12
- Publication Date
- 2025-08-26
- Estimated Expiration
- 2045-06-12
AI Technical Summary
The prior art is difficult to achieve accurate monitoring of the health status of lithium-ion batteries in the electric bicycle scenario, especially when the BMS chip computing power and data acquisition rate are low, it is impossible to effectively identify the actual health status of the battery, resulting in delays or early scrapping warnings.
Using an empirical model and capacity calibration method, by obtaining the historical and current SOC data of the electric bicycle battery, the user's habitual charging depth and charging nominal capacity are calculated, the capacity attenuation experience model is established, the number of cycles is corrected, and the battery health status is achieved.
It improves the accuracy of estimating the health status of lithium-ion batteries of electric bicycles, is suitable for low acquisition rate BMS scenarios, and realizes "one vehicle, one monitoring", which helps consumers and management departments to effectively maintain and supervise the batteries.
Smart Images
Figure CN120294611B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of lithium battery electric bicycles and relates to a health status assessment method, system, equipment and storage medium for electric bicycles. Background Art
[0002] Electric bicycles, with their convenience and affordability, are gaining popularity among consumers as a means of transportation for short distances. The physical and chemical properties of lithium-ion batteries dictate that their use must be regulated within certain limits; otherwise, battery aging will accelerate and even increase the risk of thermal runaway and fire. However, due to the complex user base, harsh charging and discharging environments, and poor maintenance conditions, there are behaviors that affect the safety of electric bicycle batteries, such as poor battery quality, unauthorized modifications, low-temperature charging, and extended use. Without adequate health status monitoring for electric bicycle batteries, there will be more cases of abuse, such as overuse. Therefore, implementing simple, accurate, and reliable battery health monitoring for each vehicle, identifying electric bicycles that have exceeded their service life, prompting consumers to promptly recycle and dispose of them, and reporting them to safety authorities for reminders and education, is crucial for reducing safety risks and improving the safety of electric bicycle use.
[0003] In recent years, a large number of studies have focused on estimating the state of health (SOH) of lithium-ion batteries, primarily categorized into direct measurement, model-based, and data-driven approaches. The direct measurement method uses a dedicated battery performance tester to measure battery characteristics such as capacity, internal resistance, power, and impedance at room temperature. The current SOH is then calculated by comparing the measured values with the battery's initial capacity, internal resistance, power, and impedance at room temperature. This method provides the most accurate SOH values, but is generally limited to offline testing and difficult to apply online. The model-based approach establishes an electrochemical or equivalent circuit model of the battery, combining state-space equations with parameter identification to derive key SOH parameters implicit in the voltage curve. A filtering algorithm is then used to update the model parameters throughout the battery's lifecycle to achieve a current SOH estimate. While the model-based approach offers certain advantages in accuracy and current performance, the parameter calculation is complex and uncertain. The data-driven approach, a typical "black box" problem, uses machine learning to understand the nonlinear mapping relationship between external battery characteristics such as voltage, current, and temperature and battery health parameters to estimate SOH. Data-driven approaches avoid the complex analysis and solution of electrochemical mechanisms, but require large amounts of data and cannot capture features beyond the data.
[0004] The above battery health estimation methods are more suitable for applications such as electric vehicles and power storage systems where BMS (Battery Management System) data acquisition accuracy and sampling rate are high. However, for e-bikes, the BMS chip computing power, voltage, current, and temperature sampling accuracy and rate are relatively poor, with data acquisition rates typically ranging from 3 to 6 minutes per cycle. Therefore, internal resistance-based health estimation methods are not well suited. Current e-bike health monitoring methods are relatively simple. They count the cumulative charge / discharge cycle reaching 90% or 60% of the full battery capacity as one cycle, and calculate the battery's cycle count. The upper limit of 1000 cycles is considered the end of life. This method completely equates battery health status with cycle count, is simple to calculate, and can provide a preliminary assessment of battery health. For a given batch of e-bikes, it is suitable for products that have remained within the specified range during production and use. However, for products with certain defects, poor performance, and user abuse, or for products with outstanding performance and better user protection, accurate health status identification cannot be accurately determined based on actual conditions, which can lead to delayed or premature battery end-of-life warnings. Summary of the Invention
[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide an electric bicycle health status assessment method, system, device and storage medium to achieve "one vehicle, one monitoring" of the battery health status in the electric bicycle scenario, help users understand the health of the battery and improve the safety of battery use.
[0006] In order to achieve the above object, the present invention adopts the following technical solutions:
[0007] A method for evaluating the health status of an electric bicycle includes the following steps:
[0008] Get the historical SOC data and current SOC data of the electric bicycle battery;
[0009] Calculate the current battery cycle count and the user's usual charging depth based on historical SOC data;
[0010] Obtain the battery's nominal charge capacity and calculate the capacity decay empirical model based on the nominal charge capacity and the user's usual charging depth;
[0011] Calculate the average health status under the current number of cycles based on the nominal charging capacity and the capacity decay empirical model;
[0012] The current charging depth and current charging capacity are obtained based on the current SOC data. Whether the current charging depth meets the calibration conditions is determined based on the current charging depth. If so, the mean health status is calibrated by charging the nominal capacity and the current charging capacity to correct the current number of cycles. If the calibration conditions are not met or the calibration is completed, the mean health status of the electric bicycle is output.
[0013] Preferably, the process of calculating the current number of cycles of the battery is: accumulating the charging depth of each charging process until the accumulated charging depth reaches a preset threshold, recording it as one cycle, resetting the accumulated charging depth, and repeating the charging depth accumulation process until all historical SOC data calculations are completed.
[0014] Preferably, the process of calculating the user's habitual charging depth is: extracting the SOC data at the beginning and end of each charge from the historical SOC data, subtracting them to obtain the charging depth of each time, calculating the mean and standard deviation of each charging depth, and based on the standard deviation, using the normal distribution standard deviation principle to eliminate outliers in the mean of each charging depth, and obtaining the mean of the charging depth under the normal distribution, and taking the mean of the charging depth under the normal distribution as the user's habitual charging depth.
[0015] Preferably, the process of obtaining the nominal charging capacity of the battery is: using a constant current and constant voltage charging mode, charging the battery from a preset SOC to a cut-off voltage with a constant current, and then switching to constant voltage charging until the current reaches a preset intensity, stopping charging the battery, and calculating the nominal charging capacity of the battery based on the constant current charging time, current and constant voltage charging time.
[0016] The preferred process for calculating the capacity fade empirical model is: using regression analysis to obtain the relationship between the battery capacity fade amount, the charging depth and the number of cycles, and obtaining the capacity fade empirical model.
[0017] Preferably, the calculation process of the current charging capacity is: using a discretized current integration method to calculate the current charging capacity, and correcting the current charging capacity to the standard temperature.
[0018] Preferably, the process of correcting the number of cycles is: correcting the number of cycles using an inverse function of an empirical capacity decay model.
[0019] An electric bicycle health status assessment system, comprising:
[0020] A data acquisition module is used to obtain historical SOC data and current SOC data of the electric bicycle battery;
[0021] User habit charging depth calculation module, used to calculate the current cycle number of the battery and the user habit charging depth based on historical SOC data;
[0022] The capacity decay empirical model calculation module is used to obtain the nominal charging capacity of the battery and calculate the capacity decay empirical model based on the nominal charging capacity and the user's usual charging depth;
[0023] The mean health state calculation module is used to calculate the mean health state under the current number of cycles based on the nominal charging capacity and the capacity decay empirical model;
[0024] The health status assessment module is used to obtain the current charging depth and current charging capacity based on the current SOC data, and determine whether the current charging depth meets the calibration conditions based on the current charging depth. If so, the mean health status is calibrated by charging the nominal capacity and the current charging capacity to correct the current number of cycles; if the calibration conditions are not met or the calibration is completed, the mean health status of the electric bicycle is output.
[0025] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the electric bicycle health status assessment method are implemented.
[0026] A computer-readable storage medium stores a computer program, which implements the steps of the electric bicycle health status assessment method when executed by a processor.
[0027] Compared with the prior art, the present invention has the following beneficial effects:
[0028] The present invention combines empirical models with capacity calibration methods to improve the accuracy of health status estimation for lithium-ion batteries used in electric bicycles in practical applications. For health status estimation in low-acquisition-rate BMS scenarios for electric bicycles, the invention has simple calculations, low health status estimation errors, and high practicality. It can achieve "one-vehicle-one-monitoring" of the health status of lithium-ion battery electric bicycles, which helps consumers and higher-level management departments to maintain, replace and supervise lithium-ion batteries in electric bicycles that have reached the scrap level. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] Figure 1 This is a flow chart of the electric bicycle health status assessment method according to Example 1 of the present invention.
[0030] Figure 2 This is a flow chart of a method for evaluating the health status of an electric bicycle according to embodiment 2 of the present invention.
[0031] Figure 3 This is a typical electric bicycle battery charging curve of the present invention.
[0032] Figure 4 This is a typical 24Ah electric bicycle battery capacity attenuation model of the present invention. DETAILED DESCRIPTION
[0033] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.
[0034] Example 1:
[0035] like Figure 1 As shown in FIG. 1 , the electric bicycle health status assessment method according to this embodiment includes the following steps:
[0036] Get the historical and current SOC data of the electric bicycle battery.
[0037] Based on historical SOC data, calculate the current cycle number of the battery and the user's habitual charging depth.
[0038] Obtain the nominal charging capacity of the battery, and calculate the capacity decay empirical model based on the nominal charging capacity and the user's usual charging depth.
[0039] Calculate the average health status under the current number of cycles based on the nominal charging capacity and the capacity decay empirical model.
[0040] The current charging depth and current charging capacity are obtained based on the current SOC data. Whether the current charging depth meets the calibration conditions is determined based on the current charging depth. If so, the mean health status is calibrated by charging the nominal capacity and the current charging capacity to correct the current number of cycles. If the calibration conditions are not met or the calibration is completed, the mean health status of the electric bicycle is output.
[0041] Example 2:
[0042] like Figure 2 As shown in FIG, the electric bicycle health status assessment method according to this embodiment is implemented based on charging depth monitoring and capacity calibration, and includes the following steps:
[0043] Step 1) After plugging in the charger, extract the battery data collected by the electric bicycle's BMS and upload the battery's historical SOC data and current SOC data to the cloud server.
[0044] Step 2) Calculate the number of cycles based on historical SOC data n k .
[0045] Step 3) Extract the user’s habitual charging depth based on historical SOC data DOC .
[0046] Step 4) Get the nominal charging capacity Q 标称 and capacity fading empirical model.
[0047] Step 5) According to the nominal capacity of the charge Q 标称 and capacity attenuation empirical model to calculate the number of cycles for this vehicle model n k The average health status under SOH value.
[0048] Step 6) Calculate the current depth of charge DOC based on the current SOC data nk , based on DOC nk If the value is greater than or equal to 95%, it is determined whether the capacity calibration can be performed. nk Greater than or equal to 95%, go to step 7); if DOC nk Less than 95%, go to step 8).
[0049] Step 7) Calculate the current charging capacity based on the current SOC data, calibrate the mean health state based on the current charging capacity, and correct the number of cycles.
[0050] Step 8) Output the mean state of health SOH value.
[0051] Specifically in this embodiment, the historical SOC data of the battery in step 1) includes historical SOC, current, time, number of cycles and capacity, and the current SOC data of the battery includes current, voltage, temperature and SOC during the current charging process. Figure 3 This is a typical e-bike battery cell charging curve. This is based on two considerations: first, the above data is common and directly measurable in an e-bike's BMS; second, battery capacity calculations require consideration of temperature, current, and time. If the BMS doesn't provide temperature data, current ambient temperature data must be collected at the charging station. Also, SOC (State of Charge) is used to measure the battery's state of charge.
[0052] Specifically, in this embodiment, the calculation logic of the number of cycles in step 2) is: accumulating the charging depth of each charging process until the accumulated charging depth reaches the charging threshold th SOC , then the number of cycles increases by one, and then the depth of charge returns to 0, and the accumulation process of the depth of charge is repeated until all historical SOC data are calculated. th SOC It’s 90%.
[0053] Specifically, in step 3), the user's customary charging depth DOC The calculation method is: first, extract the SOC data at the beginning and end of each charge from the historical SOC data, and subtract them to get the depth of charge for each time, as shown in the following example: i The depth of charge is DOCi , Secondly, calculate the mean and standard deviation of the depth of charge. The mean calculation formula is , standard deviation calculation formula , i Represents the number of charging times, which is the number of samples when calculating the mean, that is, the statistics The average value is calculated based on the depth of charge data of each charging process.
[0054] Finally, based on the standard deviation, the 3σ principle of normal distribution is used to eliminate outliers in the mean of each charging depth, and the charging depth under normal distribution is obtained. DOC The mean of the charging depth under normal distribution DOC The average of the values is taken as the user’s habitual charging depth.
[0055] Specifically, in step 4), the nominal charging capacity Q 标称 The acquisition method is: use constant current and constant voltage charging mode, charge the battery from 0% SOC at 0.125C constant current to the cut-off voltage, then switch to constant voltage charging until the current reaches 0.05C, stop charging the battery, and calculate the nominal charging capacity based on the constant current charging time, current and constant voltage charging time. Q 标称 , nominal charging capacity Q 标称 It is the charging capacity of a new battery under standard conditions (25°C).
[0056] The capacity attenuation empirical model is obtained by using regression analysis to obtain the relationship between battery capacity attenuation and charging depth and cycle number. , we get the capacity attenuation empirical model, Q loss is the current battery capacity attenuation, that is Q loss Add the current battery capacity and convert to standard temperature to get Q 标称 . is the capacity decay rate related to the depth of charge, b It is the relationship factor between capacity attenuation and cycle number.
[0057] Typical 24Ah electric bicycle battery capacity attenuation model is as follows Figure 4 As shown, Figure 4 middle, Q is the battery capacity, unit / Ah, cycle is the number of cycles, unit / time, T is the temperature, unit / ℃, showing the attenuation model of battery capacity Q with temperature T and cycle number cycle.
[0058] Specifically, in step 4), the average health status SOH The value is calculated as: .
[0059] Specifically, in step 7), the current charging capacity is calculated using the discrete current integration method, and the current charging capacity is corrected to the standard temperature. The calculation formula for the current charging capacity is: , The temperature correction coefficient is used to normalize the current charging capacity to the capacity at the standard temperature. Q ini The initial capacity of the current charge is generally less than or equal to the capacity at 5% SOC. It is calculated by multiplying the initial SOC by the nominal charge capacity. The smaller the initial SOC, the more accurate the current charge capacity correction. I is the charging current, is the charging time corresponding to the charging current.
[0060] The formula for calibrating the mean health state is:
[0061] In addition, the correction formula for the number of cycles is , the formula is The inverse function of .
[0062] Example 3:
[0063] In this embodiment, an electric bicycle health status assessment system is provided, which can be used to implement the above-mentioned electric bicycle health status assessment method. Specifically, the electric bicycle health status assessment system includes a data acquisition module, a user habit charging depth calculation module, a capacity attenuation empirical model calculation module, a mean health status calculation module and a health status assessment module.
[0064] Among them, the data acquisition module is used to obtain the historical SOC data and current SOC data of the electric bicycle battery.
[0065] The user habitual charging depth calculation module is used to calculate the current cycle number of the battery and the user habitual charging depth based on historical SOC data.
[0066] The capacity attenuation empirical model calculation module is used to obtain the nominal charging capacity of the battery and calculate the capacity attenuation empirical model based on the nominal charging capacity and the user's habitual charging depth.
[0067] The mean health state calculation module is used to calculate the mean health state under the current number of cycles based on the nominal charging capacity and the capacity decay empirical model.
[0068] The health status assessment module is used to obtain the current charging depth and current charging capacity based on the current SOC data, and determine whether the current charging depth meets the calibration conditions based on the current charging depth. If so, the mean health status is calibrated by charging the nominal capacity and the current charging capacity to correct the current number of cycles; if the calibration conditions are not met or the calibration is completed, the mean health status of the electric bicycle is output.
[0069] Example 4:
[0070] In this embodiment, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs), or a processor that can be used to store a computer program. A processor (e.g., a FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., is the computing core and control core of the terminal and is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to implement a corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of an electric bicycle health status assessment method, including: obtaining historical SOC data and current SOC data of an electric bicycle battery; calculating the current cycle number and the user's customary charging depth of the battery based on the historical SOC data; obtaining the battery's nominal charge capacity, and calculating a capacity decay empirical model based on the nominal charge capacity and the user's customary charging depth; calculating the average health status under the current cycle number based on the nominal charge capacity and the capacity decay empirical model; obtaining the current charge depth and current charge capacity based on the current SOC data, and determining whether the current charge depth meets the calibration condition based on the current charge depth. If so, calibrating the average health status using the nominal charge capacity and the current charge capacity to correct the current cycle number; and outputting the average health status of the electric bicycle if the calibration condition is not met or the calibration is completed.
[0071] Example 5:
[0072] In this embodiment, a computer-readable storage medium (Memory) is provided. The computer-readable storage medium is a memory device in a terminal device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media in the terminal device and, of course, extended storage media supported by the terminal device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device.
[0073] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the electric bicycle health status assessment method in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and execute the following steps: obtaining historical SOC data and current SOC data of the electric bicycle battery; calculating the current cycle number and user's habitual charging depth of the battery based on the historical SOC data; obtaining the battery's nominal charging capacity, and calculating a capacity attenuation empirical model based on the nominal charging capacity and the user's habitual charging depth; calculating the mean health status under the current cycle number based on the nominal charging capacity and the capacity attenuation empirical model; obtaining the current charging depth and current charging capacity based on the current SOC data, and judging whether the current charging depth meets the calibration conditions based on the current charging depth; if so, calibrating the mean health status through the nominal charging capacity and the current charging capacity to correct the current cycle number; if the calibration conditions are not met or the calibration is completed, outputting the mean health status of the electric bicycle.
[0074] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application 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, optical storage, etc.) containing computer-usable program code.
[0075] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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.
[0076] 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.
[0077] 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.
[0078] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.
[0079] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.
[0080] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description.
Claims
1. A method for evaluating the health status of an electric bicycle, characterized in that: The following processes are included: Get the historical SOC data and current SOC data of the electric bicycle battery; Calculate the current battery cycle count and the user's usual charging depth based on historical SOC data; Obtain the battery's nominal charge capacity and calculate the capacity decay empirical model based on the nominal charge capacity and the user's usual charging depth; Calculate the average health status under the current number of cycles based on the nominal charging capacity and the capacity decay empirical model; The current charging depth and current charging capacity are obtained based on the current SOC data. The current charging depth is determined based on the current charging depth to see if it meets the calibration conditions. If so, the mean health status is calibrated using the nominal charging capacity and the current charging capacity to correct the current cycle number. If the calibration conditions are not met or the calibration is completed, the average health status of the electric bicycle is output.
2. The electric bicycle health status assessment method according to claim 1, characterized in that: The process of calculating the current number of cycles of the battery is as follows: the charging depth of each charging process is accumulated until the accumulated charging depth reaches the preset threshold, which is recorded as one cycle, the accumulated charging depth is reset, and the charging depth accumulation process is repeated until all historical SOC data calculations are completed.
3. The electric bicycle health status assessment method according to claim 1, characterized in that: The process of calculating the user's habitual charging depth is as follows: extract the SOC data at the beginning and end of each charge from the historical SOC data, subtract them to obtain the charging depth of each time, calculate the mean and standard deviation of each charging depth, and based on the standard deviation, use the normal distribution standard deviation principle to eliminate outliers in the mean of each charging depth, and obtain the mean of the charging depth under the normal distribution. The mean of the charging depth under the normal distribution is used as the user's habitual charging depth.
4. The electric bicycle health status assessment method according to claim 1, characterized in that: The process of obtaining the nominal charging capacity of the battery is as follows: adopt the constant current and constant voltage charging mode, charge the battery from the preset SOC to the cut-off voltage with constant current, and then switch to constant voltage charging until the current reaches the preset intensity, then stop charging the battery, and calculate the nominal charging capacity of the battery based on the constant current charging time, current and constant voltage charging time.
5. The electric bicycle health status assessment method according to claim 1, characterized in that: The process of calculating the capacity fade empirical model is: using regression analysis to obtain the relationship between the battery capacity fade amount, the charging depth and the number of cycles, and obtaining the capacity fade empirical model.
6. The electric bicycle health status assessment method according to claim 1, characterized in that: The calculation process of the current charging capacity is as follows: the current charging capacity is calculated using the discretized current integration method, and the current charging capacity is corrected to the standard temperature.
7. The electric bicycle health status assessment method according to claim 1, characterized in that: The process of correcting the number of cycles is: using the inverse function of the capacity decay empirical model to correct the number of cycles.
8. An electric bicycle health status assessment system, characterized in that: include: A data acquisition module is used to obtain historical SOC data and current SOC data of the electric bicycle battery; User habit charging depth calculation module, used to calculate the current cycle number of the battery and the user habit charging depth based on historical SOC data; The capacity decay empirical model calculation module is used to obtain the nominal charging capacity of the battery and calculate the capacity decay empirical model based on the nominal charging capacity and the user's usual charging depth; The mean health state calculation module is used to calculate the mean health state under the current number of cycles based on the nominal charging capacity and the capacity decay empirical model; The health status assessment module is used to obtain the current charge depth and current charge capacity based on the current SOC data, and determine whether the current charge depth meets the calibration conditions based on the current charge depth. If so, the mean health status is calibrated by charging the nominal capacity and the current charge capacity to correct the current cycle number; If the calibration conditions are not met or the calibration is completed, the average health status of the electric bicycle is output.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the electric bicycle health status assessment method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the electric bicycle health status assessment method according to any one of claims 1 to 7 are implemented.
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