Electric bicycle health state assessment method, system and device and storage medium

Abstract

The invention discloses an electric bicycle health state evaluation method, system and device and a storage medium, and belongs to the field of lithium battery electric bicycles, and the method comprises the following steps: obtaining historical SOC data and current SOC data of an electric bicycle battery; calculating the current cycle index of the battery, and calculating the habitual charging depth of the user; obtaining the charging nominal capacity of the battery, and calculating to obtain a capacity attenuation empirical model; calculating a mean health state under the current cycle index; the current charging depth and the current charging capacity are calculated, whether the current charging depth meets the calibration condition or not is judged, and if yes, the average health state is calibrated through the charging nominal capacity and the current charging capacity, and the current cycle number is corrected; and if the calibration condition is not met or the calibration is completed, outputting the average health state of the electric bicycle. According to the invention, the battery health state can be monitored in an electric bicycle scene, a user is helped to know the health degree of the battery, and the use safety of the battery is improved.

Description

Technical Field

[0001] The present invention belongs to the field of lithium battery electric bicycles, and relates to a method, a system, a device and a storage medium for evaluating the health state of an electric bicycle. Background Art

[0002] Due to its convenient and economical advantages, electric bicycles are favored by consumers as a means of transportation for short-distance travel. The physical and chemical properties of lithium-ion batteries determine that their usage conditions need to be regulated within a certain boundary. Otherwise, it will accelerate battery aging and even increase the risks of thermal runaway and fire. However, due to the complex user group, harsh charging and discharging environment, and poor maintenance conditions of electric bicycles, there will be behaviors that affect the battery safety of electric bicycles, such as poor battery quality, unauthorized modification, low-temperature charging, and long service life. If no health state prompt is given to the electric bicycle battery, there will be more situations of over-service and other abuses. Therefore, it is of great significance to implement simple, accurate, and reliable vehicle-by-vehicle monitoring of the battery health state, identify electric bicycles with over-service behaviors, remind consumers to recycle and dispose of them in time, and report to the safety department for reminder and education, so as to reduce safety risks and improve the use safety of electric bicycles.

[0003] In recent years, there have been a large number of studies on the estimation of the health state of lithium-ion batteries, mainly divided into direct measurement methods, model methods, and data-driven methods. The direct measurement method uses a special battery performance tester to measure the battery capacity, internal resistance, power, impedance and other characteristics at room temperature, and compares and calculates them with the initial capacity, internal resistance, power, impedance and other performance parameters of the battery at room temperature to obtain the current health state. The health state value obtained by this method is the most accurate, but generally it is only limited to offline testing and is difficult to apply online. The model method is to establish an electrochemical model or an equivalent circuit model of the battery, combine the state space equation and parameter identification to obtain the key health state parameters hidden in the voltage curve, and use a filtering algorithm to update the model parameter values in the whole life cycle to realize the current estimation of the health state. The model method has certain current and accuracy advantages, but the parameter calculation is relatively complex and uncertain. The data-driven method is a typical "black box" problem, and it estimates the health state by using machine learning to master the non-linear mapping relationship between the external characteristic parameters of the battery such as voltage, current, and temperature and the battery health characteristic parameters. The data-driven method avoids the analysis and solution process of complex electrochemical mechanisms, but requires a large amount of data and cannot express the characteristics other than the data.

[0004] The above battery health status estimation method is more suitable for electric vehicles, power storage and other BMS (Battery Management System) applications with high acquisition accuracy and sampling rate. For electric bicycle scenarios, the BMS chip computing power, voltage, current, temperature sampling accuracy and rate are poor, and the data acquisition rate is usually 3~6min / time, so the health status estimation method based on internal resistance cannot be well applied. The current electric bicycle health status monitoring method is relatively simple. By counting the cumulative charge / discharge reaching 90% or 60% of the full battery capacity as one cycle, the number of battery cycles is calculated, and the upper limit of the cycle reaches 1000 times, which is considered to be the end of life. This method completely equates the battery health status with the number of cycles, is easy to calculate, and can preliminarily judge the battery health status. For a certain batch of electric bicycles, it is suitable for products that are maintained within the range specified in the manual during the production and use stages. However, for products with certain defects, worse performance and user abuse, or products with outstanding performance and better user protection, it is impossible to accurately identify the health status according to the actual situation, which is easy to cause delayed or early battery scrapping 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: A method for evaluating the health status of an electric bicycle includes the following steps: Get the historical SOC data and current SOC data of the electric bicycle battery; Calculate the current battery cycle count and the user's habitual charging depth based on historical SOC data; 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 habitual charging depth; Calculate the average health status under the current number of cycles based on the nominal charging capacity and the capacity attenuation empirical model; The current charging depth and current charging capacity are obtained according to the current SOC data, and whether the current charging depth meets the calibration conditions is determined according to the current charging depth. If so, the mean health state 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 state of the electric bicycle is output.

[0007] Preferably, the process of calculating the current cycle number of the battery is as follows: Accumulate the charge depth of each charging process until the accumulated charge depth reaches a preset threshold, which is recorded as one cycle. Reset the accumulated charge depth and repeat the process of accumulating the charge depth until all historical SOC data calculations are completed.

[0008] Preferably, the process of calculating the user's habitual charge depth is as follows: Extract the SOC data at the start and end of each charge from the historical SOC data, subtract them to obtain the charge depth of each charge, calculate the mean and standard deviation of each charge depth. Based on the standard deviation, use the normal distribution standard deviation principle to eliminate the outliers in the mean of each charge depth, and obtain the mean of the charge depth under the normal distribution. Take the mean of the charge depth under the normal distribution as the user's habitual charge depth.

[0009] Preferably, the process of obtaining the nominal charge capacity of the battery is as follows: Adopt the constant current and constant voltage charging mode, charge the battery from the preset SOC at a constant current until the cut-off voltage, then switch to constant voltage charging until the current reaches the preset intensity, and then stop charging the battery. Calculate the nominal charge capacity of the battery according to the time, current of the constant current charging and the time of the constant voltage charging.

[0010] Preferably, the process of calculating the capacity attenuation empirical model is as follows: Use regression analysis to obtain the relationship between the battery capacity attenuation and the charge depth and cycle number, and obtain the capacity attenuation empirical model.

[0011] Preferably, the process of calculating the current charge capacity is as follows: Calculate the current charge capacity by using the discrete current integration method and correct the current charge capacity to the standard temperature.

[0012] Preferably, the process of correcting the cycle number is as follows: Use the inverse function of the capacity attenuation empirical model to correct the cycle number.

[0013] An electric bicycle health status evaluation system, comprising: A data acquisition module, configured to acquire historical SOC data and current SOC data of an electric bicycle battery; A user habitual charge depth calculation module, configured to calculate the current cycle number and user habitual charge depth of the battery based on the historical SOC data; A capacity attenuation empirical model calculation module, configured to obtain the nominal charge capacity of the battery and calculate the capacity attenuation empirical model according to the nominal charge capacity and the user habitual charge depth; A mean health status calculation module, configured to calculate the mean health status at the current cycle number according to the nominal charge capacity and the capacity attenuation empirical model; A health status evaluation module is used to obtain the current charge depth and the current charge capacity according to the current SOC data, determine whether the current charge depth meets the calibration condition based on the current charge depth. If it meets the condition, the average health status is calibrated through the nominal charge capacity and the current charge capacity, and the current cycle count is corrected. If the calibration condition is not met or the calibration is completed, the average health status of the electric bicycle is output.

[0014] A computer device includes 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 evaluation method are implemented.

[0015] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the electric bicycle health status evaluation method are implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention combines an empirical model with a capacity calibration method, improving the accuracy of estimating the health status of lithium-ion batteries used in electric bicycles in practical applications. For the health status estimation in the scenario of a low acquisition rate BMS for electric bicycles, the calculation is simple, the health status estimation error is low, and the practicability is high. It can achieve "one vehicle, one monitoring" of the health status of lithium-ion battery electric bicycles, which helps consumers and superior management departments maintain, replace, and supervise lithium-ion batteries of electric bicycles that have reached the end-of-life level. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the electric bicycle health status evaluation method according to Embodiment 1 of the present invention.

[0018] Figure 2 is a flowchart of the electric bicycle health status evaluation method according to Embodiment 2 of the present invention.

[0019] Figure 3 is a typical charging curve of an electric bicycle battery cell of the present invention.

[0020] Figure 4 is a typical battery capacity attenuation model of a 24Ah electric bicycle of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application.

[0022] Embodiment 1: As Figure 1 shown, the electric bicycle health status evaluation method described in this embodiment includes the following processes: Obtain the historical SOC data and current SOC data of the electric bicycle battery.

[0023] Based on the historical SOC data, calculate the current cycle count and the user's habitual charging depth of the battery.

[0024] Obtain the nominal charging capacity of the battery, and calculate the capacity attenuation empirical model according to the nominal charging capacity and the user's habitual charging depth.

[0025] According to the nominal charging capacity and the capacity attenuation empirical model, calculate the average state of health at the current cycle count.

[0026] Obtain the current charging depth and current charging capacity according to the current SOC data, and determine whether the current charging depth meets the calibration condition based on the current charging depth. If it meets, calibrate the average state of health through the nominal charging capacity and the current charging capacity, and correct the current cycle count; if it does not meet the calibration condition or the calibration is completed, output the average state of health of the electric bicycle.

[0027] Embodiment 2: As Figure 2 shown, the method for evaluating the health state of the electric bicycle described in this embodiment is implemented based on charging depth monitoring and capacity calibration, and includes the following steps: Step 1), after plugging in the charger, extract the battery data collected by the electric bicycle BMS, and upload the historical SOC data and current SOC data of the battery to the cloud server.

[0028] Step 2), calculate the cycle count based on the historical SOC data n k .

[0029] Step 3), extract the user's habitual charging depth based on the historical SOC data DOC .

[0030] Step 4), obtain the nominal charging capacity Q 标称 and the capacity attenuation empirical model.

[0031] Step 5), according to the nominal charging capacity Q 标称 and the capacity attenuation empirical model, calculate the average state of health value of this model at the current cycle count n k under SOH value.

[0032] Step 6), calculate the current charging depth DOC according to the current SOC data nk , and determine whether capacity calibration can be performed based on whether DOC nk is greater than or equal to 95%. If DOCnk Greater than or equal to 95%, go to step 7); if the DOC nk Is less than 95%, go to step 8).

[0033] Step 7), calculate the current charging capacity according to the current SOC data, and based on the current charging capacity, calibrate the average state of health and correct the number of cycles.

[0034] Step 8), output the average state of health SOH value.

[0035] 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 Is the charging curve of a typical electric bicycle cell. Considering the following two aspects, one, the above data are common data in the electric bicycle BMS and can be directly measured; two, the battery capacity calculation needs to consider temperature, current, and time. If the BMS does not provide temperature data, the current ambient temperature data needs to be collected on the charging pile side; SOC (State of Charge; the state of charge of the battery).

[0036] Specifically in this embodiment, the calculation logic of the number of cycles in step 2) is: accumulate the charging depth of each charging process until the accumulated charging depth reaches the charging threshold th SOC , then the number of cycles is incremented by one, and then the charging depth is reset to 0, and the process of accumulating the charging depth is repeated until all historical SOC data is calculated. Preferably, th SOC Is 90%.

[0037] Specifically in this embodiment, the user's habitual charging depth in step 3) DOC The calculation method is: First, extract the SOC data at the start and end of each charge from the historical SOC data, and subtract them to obtain the charging depth of each charge. For example, for the i The charging depth of the th charge is DOC i , secondly, calculate the mean and standard deviation of the charging depth of this charge. The mean calculation formula , the standard deviation calculation formula , i Represents the number of charges, which is the sample size when calculating the mean, that is, the charging depth data of Charging processes are counted to calculate the mean.

[0038] Finally, based on the standard deviation, use the 3σ principle of the normal distribution to eliminate the outliers in the mean of the charging depth of each charge, and obtain the charging depth under the normal distribution DOCThe mean of the charging depth under normal distribution DOC The average of the values ​​is taken as the user’s habitual charging depth.

[0039] Specifically, in step 4), the charging nominal capacity Q 标称 The acquisition method is: using constant current and constant voltage charging mode, charging the battery from 0% SOC at 0.125C constant current to the cut-off voltage, then switching 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).

[0040] 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.

[0041] Typical 24Ah electric bicycle battery capacity decay 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 / times, T is the temperature, unit / ℃, and shows the attenuation model of the battery capacity Q with temperature T and cycle number cycle.

[0042] Specifically, in step 4), the mean health status SOH The value is calculated as: .

[0043] Specifically, in step 7), the current charging capacity is calculated by using the discrete current integration method, and the current charging capacity is corrected to the standard temperature. The calculation formula of the current charging capacity is: , is the temperature correction coefficient. Since the battery capacity increases with increasing temperature and decreases with decreasing temperature, the current charging capacity is normalized to the capacity at the standard temperature through the temperature correction coefficient. Qini is the initial capacity of the current charge, generally the capacity at SOC less than or equal to 5%, calculated by multiplying the initial SOC by the nominal charge capacity. The smaller the initial SOC, the more accurate the correction of the current charge capacity. The I is the charging current, is the charging time corresponding to the charging current.

[0044] The formula for calibrating the mean state of health is:

[0045] In addition, the correction formula for the number of cycles is , and this formula is the inverse function of.

[0046] Embodiment 3: In this embodiment, a health state evaluation system for an electric bicycle is provided. This health state evaluation system for an electric bicycle can be used to implement the above-mentioned health state evaluation method for an electric bicycle. Specifically, this health state evaluation system for an electric bicycle includes a data acquisition module, a user habit charging depth calculation module, a capacity attenuation empirical model calculation module, a mean state of health calculation module, and a health state evaluation module.

[0047] Among them, the data acquisition module is used to acquire the historical SOC data and the current SOC data of the electric bicycle battery.

[0048] The user habit charging depth calculation module is used to calculate the current number of cycles and the user habit charging depth of the battery based on the historical SOC data.

[0049] The capacity attenuation empirical model calculation module is used to obtain the nominal charge capacity of the battery and calculate the capacity attenuation empirical model according to the nominal charge capacity and the user habit charging depth.

[0050] The mean state of health calculation module is used to calculate the mean state of health at the current number of cycles according to the nominal charge capacity and the capacity attenuation empirical model.

[0051] The health state evaluation module is used to obtain the current charging depth and the current charge capacity according to the current SOC data, determine whether the current charging depth meets the calibration condition according to the current charging depth. If it meets, the mean state of health is calibrated through the nominal charge capacity and the current charge capacity, and the current number of cycles is corrected; if the calibration condition is not met or the calibration is completed, the mean state of health of the electric bicycle is output.

[0052] Embodiment 4: In this embodiment, a terminal device is provided. The terminal device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. 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 may 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. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiments of the present invention can be used for the operation of the method for evaluating the health status of an electric bicycle, including: obtaining historical SOC data and current SOC data of the electric bicycle battery; calculating the current cycle number of the battery and the user's habitual charging depth based on the historical SOC data; obtaining the nominal charging capacity of the battery, and calculating a capacity attenuation empirical model according to the nominal charging capacity and the user's habitual charging depth; calculating the average health status at the current cycle number according to the nominal charging capacity and the capacity attenuation empirical model; obtaining the current charging depth and current charging capacity according to the current SOC data, and determining whether the current charging depth meets the calibration condition according to the current charging depth. If it meets the condition, the average health status is calibrated by the nominal charging capacity and the current charging capacity, and the current cycle number is corrected; if the calibration condition is not met or the calibration is completed, the average health status of the electric bicycle is output.

[0053] Embodiment 5: In this embodiment, a computer-readable storage medium (Memory) is provided. The computer-readable storage medium is a memory device in a terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by a processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory (Random Access Memory), or a non-volatile memory, such as at least one disk memory.

[0054] One or more instructions stored in the computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the electric bicycle health status evaluation method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: obtaining the historical SOC data and the current SOC data of the electric bicycle battery; calculating the current cycle number of the battery and the user's habitual charging depth based on the historical SOC data; obtaining the nominal charging capacity of the battery, and calculating a capacity attenuation empirical model according to the nominal charging capacity and the user's habitual charging depth; calculating the average health status at the current cycle number according to the nominal charging capacity and the capacity attenuation empirical model; obtaining the current charging depth and the current charging capacity according to the current SOC data, and determining whether the current charging depth meets the calibration condition based on the current charging depth. If it meets, calibrate the average health status through the nominal charging capacity and the current charging capacity, and correct the current cycle number; if it does not meet the calibration condition or the calibration is completed, output the average health status of the electric bicycle.

[0055] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, optical memories, etc.) containing computer-usable program codes.

[0056] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in a process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0057] These computer program instructions can 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, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in a process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in a process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0059] In the above embodiments of the present application, the descriptions of the respective embodiments have their own focuses. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0060] The above is only the preferred embodiment of the present application. It should be noted that for those of ordinary skill in the art in the technical field of the present application, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

[0061] It should be understood that the above description is for the purpose of illustration rather than limitation. By reading the above description, many embodiments and many applications other than the provided examples will be obvious to those skilled in the art.

Claims

1. An electric bicycle health status evaluation method, characterized in that, The process includes: Get the historical SOC data and current SOC data of the electric bicycle battery; Calculate the current battery cycle count and the user's habitual charging depth based on historical SOC data; 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 habitual charging depth; Calculate the average health status under the current number of cycles based on the nominal charging capacity and the capacity attenuation empirical model; The current charging depth and current charging capacity are obtained according to the current SOC data, and whether the current charging depth meets the calibration conditions is determined according to the current charging depth. If so, the mean health state 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 average health status of the electric bicycle is output.

2. The method for evaluating the health status of an electric bicycle 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 method for evaluating the health status of an electric bicycle 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 charging 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, obtain the mean of the charging depth under the normal distribution, and use the mean of the charging depth under the normal distribution as the user's habitual charging depth.

4. The method for evaluating the health status of an electric bicycle 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 method for evaluating the health status of an electric bicycle according to claim 1, wherein The process of calculating the capacity attenuation empirical model is as follows: using regression analysis to obtain the relationship between the battery capacity attenuation amount, the charging depth and the number of cycles, and obtaining the capacity attenuation empirical model.

6. The method for evaluating the health status of an electric bicycle according to claim 1, wherein The calculation process of the current charging capacity is: the current charging capacity is calculated by using the discrete current integration method, and the current charging capacity is corrected to the standard temperature.

7. The method for evaluating the health state of an electric bicycle according to claim 1, wherein The process of correcting the number of cycles is to correct the number of cycles using the inverse function of the capacity decay empirical model.

8. An electric bicycle health status evaluation 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; A 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; 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; 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 evaluation module is used to obtain the current charge depth and the current charge capacity according to the current SOC data, judge whether the current charge depth meets the calibration condition according to the current charge depth. If it meets the condition, the average health status is calibrated through the nominal charge capacity and the current charge capacity, and the current cycle number is corrected; If the calibration condition is 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, characterized in that, When the processor executes the computer program, the steps of the electric bicycle health status evaluation method described in 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 the processor, the steps of the electric bicycle health status evaluation method described in any one of claims 1 to 7 are implemented.

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