A method for testing the working conditions of an electric bicycle

Abstract

The present invention relates to the technical field of battery measurement, and particularly relates to a method for testing the working conditions of an electric bicycle. This method obtains the current data of the battery during the driving process of the electric bicycle after each full charge; according to the change situation of the current data, obtains the battery deterioration degree and the non-standard driving degree of the electric bicycle; according to the time interval between the end moment of each driving process and the start moment of the electric bicycle charging, and the charging duration difference between each charging of the electric bicycle and its adjacent previous charging, obtains the battery aging degree; according to the battery deterioration degree, the non-standard driving degree and the battery aging degree, corrects the working condition test parameters of the electric bicycle to obtain the corrected working condition test parameters of the electric bicycle. The present invention accurately evaluates the quality of the electric bicycle by accurately obtaining the corrected working condition test parameters, and ensures that the quality of the electric bicycles entering the market is qualified.

Description

Technical Field

[0001] The present invention relates to the technical field of battery measurement, and particularly to a method for testing the working conditions of an electric bicycle. Background Art

[0002] With the aggravation of urban traffic congestion problems and the improvement of environmental protection awareness, electric bicycles, as a green travel mode, are becoming more and more popular, and the importance of electric bicycles in urban traffic is also becoming more and more significant. In order to meet the needs of users for electric bicycles, manufacturers of electric bicycles need to conduct working condition tests on electric bicycles before leaving the factory to ensure the operation quality of electric bicycles, thereby promoting the healthy development of the electric bicycle industry. It is known that the power for an electric bicycle to run is a battery. Therefore, by measuring the battery of the electric bicycle, the working conditions of the electric bicycle can be reflected.

[0003] In the existing method, the working conditions of an electric bicycle are evaluated in a standardized manner by analyzing the difference between the output power and the input power of the battery of the electric bicycle. However, in actual situations, with the increase in the running time of the electric bicycle and bad behaviors such as irregular riding of the electric bicycle, the battery of the electric bicycle will deteriorate to a certain extent, resulting in a deviation in the actual discharge current of the battery of the electric bicycle, and further leading to inaccurate standardized evaluation of the working conditions of the electric bicycle, and thus unable to accurately analyze the operation quality of the electric bicycle. Summary of the Invention

[0004] In order to solve the technical problem of inaccurate standardized evaluation of the working conditions of an electric bicycle, the purpose of the present invention is to provide a method for testing the working conditions of an electric bicycle, and the specific technical solution adopted is as follows:

[0005] An embodiment of the present invention provides a method for testing the working conditions of an electric bicycle, and the method includes the following steps:

[0006] Obtain the current data of the battery at each moment during the driving process of the electric bicycle after each full charge for a preset number of times; wherein, the speed during the driving process of the electric bicycle after each full charge is the same, and the driving process after each full charge is a complete driving process;

[0007] According to the change situation of the current data during each driving process, obtain the reference service life of the battery for each driving process; according to the difference in the reference service life of the battery between the last driving process and all other driving processes, obtain the degree of battery deterioration of the electric bicycle;

[0008] According to the distribution situation of the current data during each driving process, obtain the degree of irregular riding of the electric bicycle;

[0009] Obtain the battery aging degree of the electric bicycle according to the time interval between the end moment of each driving process and the start moment of charging the electric bicycle, as well as the difference in charging duration between each charge of the electric bicycle and its previous adjacent charge.

[0010] According to the battery deterioration degree, the non-standard driving degree, and the battery aging degree, correct the working condition test parameters of the electric bicycle to obtain the corrected working condition test parameters of the electric bicycle.

[0011] Further, the method for obtaining the reference service life of the battery is as follows:

[0012] For any current data in any driving process, construct a preset window for this current data, and obtain the charge balance coefficient at the moment corresponding to this current data according to the mean and variance of the current data within the preset window; wherein, both the mean and variance of the current data within the preset window are negatively correlated with the charge balance coefficient.

[0013] Obtain the charge balance coefficient at the moment corresponding to each current data in this driving process.

[0014] When the charge balance coefficient is greater than the preset charge balance coefficient threshold, regard the corresponding current data as the target current data.

[0015] Obtain the duration between the start moment of this driving process and the moment corresponding to the first occurrence of the target current data as the reference service life of the battery for this driving process.

[0016] Further, the method for obtaining the battery deterioration degree is as follows:

[0017] Obtain the mean value of the reference service life of the battery for all driving processes except the last driving process as the reference duration.

[0018] Take the normalized result of the difference between the reference duration and the reference service life of the battery for the last driving process as the battery deterioration degree of the electric bicycle.

[0019] Further, the method for obtaining the non-standard driving degree is as follows:

[0020] For any one of the preset number of driving processes, determine the peak current data in the current data of this driving process through peak detection technology.

[0021] For any current data in this driving process, construct a reference window for this current data, and obtain the fluctuation anomaly coefficient of this current data according to the change rate between the maximum current data and the minimum current data in the reference window, as well as the fluctuation degree of the peak current data.

[0022] Obtain the fluctuation anomaly coefficient of each current data during the driving process, and regard the current data corresponding to the fluctuation anomaly coefficient greater than the preset fluctuation anomaly threshold as abnormal current data;

[0023] According to the distribution of abnormal current data during the driving process, obtain the random driving degree of the driving process;

[0024] Take the result of normalizing the sum of the random driving degrees of all driving processes in the preset number of times as the non-standard driving degree of the electric bicycle.

[0025] Furthermore, the method for obtaining the fluctuation anomaly coefficient is as follows:

[0026] For any current data in any driving process, obtain the difference between the maximum current data and the minimum current data in the reference window of this current data as the first difference;

[0027] Obtain the duration between the moments corresponding to the maximum current data and the minimum current data in the reference window of this current data as the first duration;

[0028] Take the ratio of the first difference to the first duration as the current reference change rate;

[0029] Obtain the variance of the peak current data in the reference window of this current data as the waveform reference value;

[0030] Take the result of normalizing the sum of the current reference change rate and the waveform reference value as the fluctuation anomaly coefficient of this current data.

[0031] Furthermore, the method for obtaining the random driving degree is as follows:

[0032] For any driving process, regard the area formed by continuous and uninterrupted abnormal current data during this driving process as the abnormal fluctuation area;

[0033] Obtain the duration of the time interval between any two adjacent abnormal fluctuation areas as the second duration;

[0034] According to the mean value of all the second durations during this driving process and the proportion of abnormal current data during this driving process, obtain the random driving degree of this driving process; among them, the mean value of the second duration is negatively correlated with the random driving degree, and the proportion of abnormal current data during the driving process is positively correlated with the random driving degree.

[0035] Furthermore, the method for obtaining the battery aging degree is as follows:

[0036] Regard a complete charging and discharging process of the electric bicycle as one charge-discharge process;

[0037] Obtain the duration of the time interval between two adjacent charge-discharge processes as the third duration;

[0038] Take the result of normalizing the sum of all the third durations as the first aging reference value;

[0039] Obtain the difference between the charging duration of each charge of the electric bicycle and the charging duration of its previous adjacent charge for a preset number of times, and all of them are used as the first difference;

[0040] Take the result of normalizing the sum of all the first differences as the second aging reference value;

[0041] Take the product of the first aging reference value and the second aging reference value as the battery aging degree of the electric bicycle.

[0042] Further, the method for obtaining the corrected working condition test parameters is as follows:

[0043] Obtain a correction weight according to the battery deterioration degree, the non-standard driving degree and the battery aging degree; wherein, the battery deterioration degree, the non-standard driving degree and the battery aging degree are all negatively correlated with the correction weight;

[0044] Take the product of the correction weight and the working condition test parameters as the corrected working condition test parameters.

[0045] Further, the method for obtaining the working condition test parameters is as follows:

[0046] Take the ratio of the output power to the input power of the electric bicycle battery as the working condition test parameter of the electric bicycle.

[0047] Further, the preset number is set to 50.

[0048] The present invention has the following beneficial effects:

[0049] According to the change of current data during each driving process, the present invention obtains the reference battery usage duration for each driving process, accurately reflecting the actual discharge duration of the battery during each driving process. Considering that the deterioration of the electric bicycle battery gradually increases with the increase of driving time, the battery deterioration degree of the electric bicycle is obtained based on the difference in the reference battery usage duration between the last driving process and all other driving processes, accurately reflecting the damage degree of battery deterioration to the battery, which is beneficial to accurately adjusting the working condition test parameters of the electric bicycle subsequently. Further, according to the distribution of current data during each driving process, the degree of irregular driving of the electric bicycle is obtained, accurately reflecting the impact brought by the irregular driving of the electric bicycle, which is also beneficial to accurately adjusting the working condition test parameters of the electric bicycle subsequently. Further, according to the time interval between the end moment of each driving process and the start moment of charging the electric bicycle, and the difference in charging duration between each charging of the electric bicycle and its previous adjacent charging, the battery aging degree of the electric bicycle is obtained, accurately reflecting the damage degree of battery aging to the battery, which is also beneficial to accurately adjusting the working condition test parameters of the electric bicycle subsequently. Therefore, according to the battery deterioration degree, the degree of irregular driving, and the battery aging degree, the working condition test parameters of the electric bicycle are corrected to accurately obtain the corrected working condition test parameters of the electric bicycle, so as to accurately evaluate the quality of the electric bicycle and avoid the unqualified quality of the produced electric bicycles, which affects the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0051] Figure 1 It is a schematic flowchart of a method for testing the working conditions of an electric bicycle provided by an embodiment of the present invention;

[0052] Figure 2 It is a flowchart of a method for obtaining the degree of irregular driving provided by an embodiment of the present invention;

[0053] Figure 3 It is a flowchart of a method for obtaining corrected working condition test parameters provided by an embodiment of the present invention;

[0054] Figure 4 It is a structural diagram of a system for testing the working conditions of an electric bicycle provided by an embodiment of the present invention;

[0055] Figure 5Schematic diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0056] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following describes in detail a method for testing the working conditions of an electric bicycle proposed according to the present invention, its specific implementation manners, structures, features and effects in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0058] The following specifically describes the specific solution of a method for testing the working conditions of an electric bicycle provided by the present invention with reference to the accompanying drawings.

[0059] Embodiment 1:

[0060] The specific scenario of this embodiment is as follows: In order to ensure the quality of electric bicycles on the market, before a manufacturer of electric bicycles produces the same batch of electric bicycles, several electric bicycles are first produced for working condition detection to analyze the quality of the electric bicycles and ensure the quality of the subsequent produced electric bicycles. In order to accurately detect the working conditions of the electric bicycles to be analyzed, each electric bicycle to be analyzed is analyzed in this embodiment. First, the current data of the battery at each moment during the driving process after each charge of each electric bicycle to be analyzed is obtained for a preset number of times; then, the current data during each driving process is analyzed to obtain the battery deterioration degree, the non-standard driving degree, and the battery aging degree of each electric bicycle to be analyzed; finally, according to the battery deterioration degree, the non-standard driving degree, and the battery aging degree, the working condition test parameters of each electric bicycle to be analyzed are corrected to obtain the corrected working condition test parameters of each electric bicycle to be analyzed, accurately analyze the quality of each electric bicycle to be analyzed, and indirectly reflect the quality of other electric bicycles in the same batch produced subsequently, ensuring that the quality of the electric bicycles entering the market meets the standards.

[0061] This embodiment proposes a method for testing the working conditions of an electric bicycle. Please refer to Figure 1 , which shows a schematic flowchart of a method for testing the working conditions of an electric bicycle provided by an embodiment of the present invention. The method includes the following steps:

[0062] Step S1: Obtain the current data of each moment of the battery during the driving process of the electric bicycle after each full charge for a preset number of times; wherein, the speed of the electric bicycle during the driving process after each full charge is the same, and the driving process after each full charge is a complete driving process.

[0063] Specifically, in order to ensure that the quality of the produced electric bicycles meets the standards, in this embodiment, it is set that the manufacturer of the electric bicycle first produces 3 electric bicycles through the same production technology and process, and then analyzes the working conditions of these 3 electric bicycles to intermittently reflect the quality of the electric bicycles produced by the manufacturer of the electric bicycle subsequently. The implementer can set the number of electric bicycles produced by the manufacturer of the electric bicycle through the same production technology and process according to the actual situation, which is not limited here.

[0064] For clear analysis, in this embodiment, any one of the 3 electric bicycles is taken as an example for analysis, and all subsequent electric bicycles refer to the selected one. It is known that with the increase of the use time of the electric bicycle and bad behaviors such as irregular riding of the electric bicycle, the battery of the electric bicycle will deteriorate to a certain extent, resulting in a deviation in the actual discharge current of the electric bicycle battery, and further leading to inaccurate standard evaluation of the working conditions of the electric bicycle, and thus unable to accurately analyze the operation quality of the electric bicycle. In order to accurately evaluate the working conditions of the electric bicycle, this embodiment needs to analyze the damage situation of the electric bicycle battery. Therefore, in this embodiment, the current data of each moment of the battery during the driving process of the electric bicycle after each full charge for a preset number of times is obtained through a Hall effect current sensor. In this embodiment, the preset number is set to 50, and at the same time, the data sampling frequency of the Hall effect current sensor is set to 1KHz. The implementer can set the size of the preset number and the data sampling frequency of the Hall effect current sensor according to the actual situation, which is not limited here. Wherein, the speed of the electric bicycle during the driving process after each full charge is the same, and the driving process after each full charge is a complete driving process. At the same time, the road conditions of each electric bicycle driving are random, and the electric bicycle can ride on flat roads, uphill roads, highways, mountain roads and other road conditions.

[0065] In order to improve the accurate analysis of the battery damage situation through the current data, in this embodiment, preprocessing operations such as denoising and calibration are performed on the obtained current data of each driving process to ensure the accuracy of the collected data.

[0066] Step S2: According to the change situation of the current data during each driving process, obtain the reference service life of the battery for each driving process; according to the difference in the reference service life of the battery between the last driving process and all other driving processes, obtain the degree of battery deterioration of the electric bicycle.

[0067] Specifically, during the driving process of the electric bicycle, when the current data gradually decreases and tends to be stable, it indicates that the battery reaches the power balance, that is, the current discharge ends. Furthermore, in this embodiment, according to the change of the current data during each driving process, the reference battery usage duration of each driving process is obtained. It is known that the battery loss gradually increases with the increase of the usage time. Therefore, when the difference in the reference battery usage duration between the last driving process of the electric bicycle and all other driving processes in the preset number of times is larger, it indicates that the battery damage of the electric bicycle is more serious. Furthermore, in this embodiment, according to the difference in the reference battery usage duration between the last driving process and all other driving processes, the battery deterioration degree of the electric bicycle is obtained. The greater the battery deterioration degree, the greater the loss degree of the battery of the electric bicycle after driving the preset number of times.

[0068] Preferably, in an implementable manner of this embodiment, the method for obtaining the reference battery usage duration is as follows: for any current data during any driving process, a preset window of this current data is constructed. In this embodiment, with this current data as the end point, the 19 current data that are temporally before this current data and the closest to this current data in time series and this current data are constructed into the preset window of this current data. For example, taking the i-th current data as an example, the area corresponding to the (i - 19)-th current data to the i-th current data is the preset window of the i-th current data. The implementer can construct the preset window of each current data according to the actual situation, which is not limited here. It should be noted that for the boundary current data in time series, if there are less than 19 current data before a certain boundary current data, the current data existing before this boundary current data and this boundary current data are constructed into the preset window of this boundary current data;

[0069] When the current data in the preset window of this current data is smaller and more stable, the moment corresponding to this current data is more likely to be the battery power balance moment. Furthermore, according to the mean and variance of the current data in the preset window, the power balance coefficient at the moment corresponding to this current data is obtained; among them, both the mean and variance of the current data in the preset window are negatively correlated with the power balance coefficient; the greater the power balance coefficient, the more likely the moment corresponding to this current data is the battery power balance moment. Among them, the calculation formula of the power balance coefficient is: B i = exp(-(μ i + S i )); in the formula, B i is the power balance coefficient of the i-th current data; μ i is the mean of the current data in the preset window of the i-th current data; S i is the variance of the current data in the preset window of the i-th current data; exp is the exponential function with the natural constant as the base;

[0070] Obtain the power balance coefficient at the moment corresponding to each current data during this driving process; when the power balance coefficient is greater than the preset power balance coefficient threshold, all the corresponding current data are used as target current data; in this embodiment, the preset power balance coefficient threshold is set to 0.8, and the implementer can set the size of the preset power balance coefficient threshold according to the actual situation, which is not limited here. Obtain the duration between the start time of this driving process and the time corresponding to the first occurrence of the target current data as the battery reference usage duration of this driving process. The greater the battery reference usage duration, the longer the time of this driving process, indirectly indicating that the battery usage performance during this driving is better.

[0071] So far, obtain the battery reference usage duration of each driving process of the electric bicycle in the preset number of times.

[0072] Preferably, in a realizable manner of this embodiment, the method for obtaining the battery deterioration degree is as follows:

[0073] Obtain the average value of the battery reference usage durations of all other driving processes except the last driving process in the preset number of times as the reference duration; the greater the difference between the reference duration and the battery reference usage duration of the last driving process, the more likely it is that the electric bicycle is affected by battery aging. Furthermore, obtain the result of normalizing the difference between the reference duration and the battery reference usage duration of the last driving process as the battery deterioration degree of the electric bicycle. Among them, the calculation formula for the battery deterioration degree is: D = norm(l ′ -l last ); in the formula, D is the battery deterioration degree; l ′ is the reference duration; l last is the battery reference usage duration of the last driving process; norm is the normalization function.

[0074] So far, obtain the battery deterioration degree of the electric bicycle.

[0075] Step S3: Obtain the non-standard driving degree of the electric bicycle according to the distribution of current data during each driving process.

[0076] Specifically, the current data waveform of the battery of an electric bicycle is relatively stable during stable driving, with a small fluctuation range. When the electric bicycle exhibits bad driving behaviors, such as sudden acceleration and deceleration, the waveform of the current data will show obvious short-term abnormal fluctuations, which will accelerate the degradation of the electric bicycle battery and reduce the performance of the battery, which may affect the overall performance and safety of the electric bicycle. Furthermore, this embodiment analyzes the irregular behavior of the electric bicycle during each driving process based on the distribution of current data during each driving process, and finally obtains the degree of irregular driving of the electric bicycle. The greater the degree of irregular driving, the greater the degree of damage to the electric bicycle battery is indirectly reflected.

[0077] Preferably, in one possible implementation of this embodiment, the method for obtaining the degree of irregular driving is described in Figure 2 , which shows a flow chart of a method for obtaining the degree of irregular driving provided by this embodiment, the method comprising the following steps:

[0078] Step S201: for any driving process in a preset number of times, peak current data in current data of the driving process is determined by peak detection technology.

[0079] By acquiring the peak current data, the change of overcurrent data during driving can be analyzed more accurately and efficiently. Among them, the peak detection technology is a well-known technology and will not be described in detail.

[0080] Step S202: For any current data in the driving process, a reference window of the current data is constructed, and a fluctuation anomaly coefficient of the current data is obtained according to the change rate between the maximum current data and the minimum current data in the reference window and the fluctuation degree of the peak current data.

[0081] For any current data during any driving process, in this embodiment, taking this current data as the end point, the 20 current data that are temporally before this current data and are the closest to this current data in time series and this current data are constructed into the reference window of this current data. For example, taking the i-th current data as an example, the area corresponding to the (i - 20)-th current data to the i-th current data is the reference window of the i-th current data. The implementer can construct the reference window of this current data according to the actual situation, which is not limited here. It should be noted that for the boundary current data in time series, if there are less than 20 current data before a certain boundary current data, then the current data existing before this boundary current data and this boundary current data are constructed into the reference window of this boundary current data. It is known that the current data fluctuations caused by non-standard driving are obvious and short-lived. Therefore, when the change rate between the maximum current data and the minimum current data in the reference window of a certain current data is faster and the fluctuation degree of the peak current data is greater, it indicates that this current data is more likely to be caused by abnormal driving behavior. Furthermore, in this embodiment, according to the change rate between the maximum current data and the minimum current data in the reference window and the fluctuation degree of the peak current data, the fluctuation anomaly coefficient of the corresponding current data is obtained. The larger the fluctuation anomaly coefficient, the more likely the corresponding current data has a fluctuation anomaly.

[0082] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the fluctuation anomaly coefficient is as follows: For any current data during any driving process, obtain the absolute value of the difference between the maximum current data and the minimum current data in the reference window of this current data as the first difference; obtain the duration between the times corresponding to the maximum current data and the minimum current data in the reference window of this current data as the first duration; if there is more than one maximum current data or minimum current data in the reference window of this current data, then obtain the duration between the times corresponding to each maximum current data and each minimum current data, and then take the minimum duration as the first duration; take the ratio of the first difference to the first duration as the current reference change rate; the larger the current reference change rate, the shorter the fluctuation of the current data, and the more likely this current data has a fluctuation anomaly. In order to further analyze the degree of fluctuation anomaly of this current data, then obtain the variance of the peak current data in the reference window of this current data as the waveform reference value; the larger the waveform reference value, the more obvious the fluctuation of the current data in the reference window of this current data, indirectly indicating that this current data is more likely to have a fluctuation anomaly. Furthermore, take the result of normalizing the sum of the current reference change rate and the waveform reference value as the fluctuation anomaly coefficient of this current data. Among them, the calculation formula of the fluctuation anomaly coefficient is: In the formula, Y j is the fluctuation anomaly coefficient of the j-th current data; H j,max is the maximum current data in the reference window of the j-th current data; Hj,min is the minimum current data in the reference window of the j-th current data; t j,(max,min) is the first duration; S j ′ is the waveform reference value; is the current reference change rate; || is the absolute value function; norm is the normalization function.

[0083] Step S203: Obtain the fluctuation anomaly coefficient of each current data during the driving process, and regard the current data corresponding to the fluctuation anomaly coefficient greater than the preset fluctuation anomaly threshold as abnormal current data.

[0084] It is known that the larger the fluctuation anomaly coefficient, the more likely the corresponding current data is abnormal. Furthermore, in this embodiment, the fluctuation anomaly coefficient of each current data during a certain driving process is obtained, and the current data corresponding to the fluctuation anomaly coefficient greater than the preset fluctuation anomaly threshold is regarded as the abnormal current data during this driving process. In this embodiment, the preset fluctuation anomaly threshold is set to 0.6, and the implementer can set the size of the preset fluctuation anomaly threshold according to the actual situation, which is not limited here.

[0085] Step S204: Obtain the random driving degree of this driving process according to the distribution of the abnormal current data during this driving process.

[0086] When there are more abnormal current data during a certain driving process and the time interval between the regions corresponding to the abnormal current data is smaller, it indicates that irregular driving behaviors frequently occur during this driving process, indirectly indicating that the random driving degree of this driving process is greater. Furthermore, in this embodiment, the random driving degree of this driving process is obtained according to the distribution of the abnormal current data during a certain driving process.

[0087] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the degree of random driving is as follows: For any driving process, the area formed by continuous and uninterrupted abnormal current data during the driving process is used as the abnormal fluctuation area; when the time interval between two adjacent abnormal fluctuation areas is shorter, it indicates that irregular driving behaviors occur more frequently; furthermore, the duration of the time interval between any two adjacent abnormal fluctuation areas is obtained as the second duration; the smaller the second duration, the more frequently irregular driving behaviors occur. Among them, for any two adjacent abnormal fluctuation areas, the duration between the end time of the previous abnormal fluctuation area and the start time of the next abnormal fluctuation area is used as the second duration. In order to accurately represent the degree of random driving during this driving process, furthermore, according to the average value of all the second durations during this driving process and the proportion of abnormal current data in this driving process, the degree of random driving of this driving process is obtained; among them, the average value of the second duration and the degree of random driving are negatively correlated, and the proportion of abnormal current data in the driving process and the degree of random driving are positively correlated. The greater the degree of random driving, the more irregular driving behaviors there are during this driving process, and the greater the possible damage to the battery of the electric bicycle. Among them, the calculation formula for the degree of random driving is: In the formula, R a is the degree of random driving of the a-th driving process; is the average value of all the second durations during the a-th driving process; α is a first preset constant, greater than 0; n a is the number of abnormal current data during the a-th driving process; N a is the number of all current data during the a-th driving process.

[0088] In this embodiment, α is set to 1 to avoid the denominator being 0. The implementer can set the size of α according to the actual situation, and it is not limited here.

[0089] Step S205: The result of normalizing the sum of the degrees of random driving of all driving processes in a preset number of times is used as the degree of irregular driving of the electric bicycle.

[0090] The impact of the irregular driving behavior of the electric bicycle on the battery is irreversible. In order to accurately determine the degree of irregular driving of the electric bicycle, furthermore, in this embodiment, the result of normalizing the sum of the degrees of random driving of all driving processes in a preset number of times is used as the degree of irregular driving of the electric bicycle. In this embodiment, the sum result of the degrees of random driving of all driving processes in a preset number of times is normalized through the norm normalization function.

[0091] Step S4: Obtain the battery aging degree of the electric bicycle according to the time interval between the end moment of each driving process and the start moment of charging the electric bicycle, and the difference in charging duration between each charge of the electric bicycle and its previous adjacent charge.

[0092] In actual situations, after each driving of the electric bicycle, that is, after the battery of the electric bicycle finishes discharging, if the electric bicycle is not charged in time, the battery may experience deep discharge, which will cause further aging of the battery. It is known that when the battery of the electric bicycle ages, the charging duration of the electric bicycle will increase. Therefore, in this embodiment, the battery aging degree of the electric bicycle is obtained according to the time interval between the end moment of each driving process and the start moment of charging the electric bicycle, and the difference in charging duration between each charge of the electric bicycle and its previous adjacent charge. The greater the battery aging degree, the greater the damage to the battery of the electric bicycle. In this embodiment, the charging system directly obtains the charging duration of each charge of the electric bicycle. Among them, the end moment of each driving process of the electric bicycle is the moment when there is no more current data generated by the battery.

[0093] Preferably, in a realizable manner of this embodiment, the method for obtaining the battery aging degree is as follows: For the convenience of description, this embodiment regards a complete charging and discharging process of the electric bicycle as one charge-discharge process; obtain the duration of the time interval between two adjacent charge-discharge processes as the third duration. Among them, the duration between the end moment of the previous charge-discharge process and the start moment of the next charge-discharge process in two adjacent charge-discharge processes is the third duration. The greater the third duration, the greater the degree of aging of the battery of the electric bicycle. In order to obtain the battery aging degree of the electric bicycle, therefore, in this embodiment, the result of normalizing the sum of all the third durations in a preset number of charge-discharge processes is used as the first aging reference value. The greater the first aging reference value, the greater the possible degree of aging of the battery of the electric bicycle. In order to more accurately obtain the battery aging degree of the electric bicycle, therefore, obtain the difference in charging duration between each charge of the electric bicycle and its previous adjacent charge in a preset number of times, and all of them are used as the first difference. The greater the first difference, the greater the battery aging degree. In order to comprehensively analyze the aging degree of the battery, therefore, the result of normalizing the sum of all the first differences is used as the second aging reference value. The greater the second aging reference value, the greater the degree of aging of the battery of the electric bicycle. Then, the product of the first aging reference value and the second aging reference value is used as the battery aging degree of the electric bicycle. Among them, the value range of the battery aging degree is between 0 and 1.

[0094] Step S5: Modify the working condition test parameters of the electric bicycle according to the battery deterioration degree, the non-standard driving degree, and the battery aging degree, and obtain the modified working condition test parameters of the electric bicycle.

[0095] In the existing method, the ratio of the output power to the input power of the electric bicycle battery is used as the working condition test parameter of the electric bicycle. Among them, the output power and the input power of the electric bicycle battery are both known. The working condition test parameter obtained by the existing method does not take into account the damage suffered by the battery itself, resulting in the working condition test parameter being unable to accurately reflect the quality of the electric bicycle. It is known that the greater the degree of battery deterioration, the greater the degree of irregular driving, and the greater the degree of battery aging, all indicate that the battery of the electric bicycle is more damaged, indirectly indicating that the possibility of deviation of the current of the electric bicycle is greater. Therefore, in this embodiment, according to the degree of battery deterioration, the degree of irregular driving, and the degree of battery aging, the working condition test parameter of the electric bicycle is corrected to obtain the corrected working condition test parameter of the electric bicycle, so as to accurately reflect the quality of the electric bicycle and indirectly infer whether the technology and process for producing the electric bicycle need to be optimized.

[0096] Preferably, in a feasible implementation manner of this embodiment, for the method of obtaining the corrected working condition test parameter, please refer to Figure 3 , which shows a flowchart of a method for obtaining a corrected working condition test parameter provided in this embodiment. The method includes the following steps:

[0097] Step S301: Obtain a correction weight according to the degree of battery deterioration, the degree of irregular driving, and the degree of battery aging; among them, the degree of battery deterioration, the degree of irregular driving, and the degree of battery aging are all negatively correlated with the correction weight.

[0098] It is known that the greater the degree of battery deterioration, the greater the degree of irregular driving, and the greater the degree of battery aging, the greater the possibility of deviation of the current of the electric bicycle, and the smaller the working condition test parameter should be, indicating that the quality of the electric bicycle is more unqualified. Therefore, in this embodiment, according to the degree of battery deterioration, the degree of irregular driving, and the degree of battery aging, a correction weight is obtained; among them, the degree of battery deterioration, the degree of irregular driving, and the degree of battery aging are all negatively correlated with the correction weight. Among them, the calculation formula of the correction weight is: In the formula, is the correction weight; D is the degree of battery deterioration; G is the degree of irregular driving; X is the degree of battery aging; norm is the normalization function.

[0099] It should be noted that the greater norm(D + G + X) is, the greater the possibility of deviation of the current of the electric bicycle is, indirectly indicating that the working condition test parameter is more inaccurate, and the working condition test parameter should be smaller. Therefore, the correction weight of the working condition test parameter is obtained by 1 - norm(D + G + X).

[0100] Step S302: Multiply the correction weight by the working condition test parameter to obtain the corrected working condition test parameter.

[0101] The smaller the corrected working condition test parameter is, the more likely there are problems with the quality of the electric bicycle.

[0102] According to the method for obtaining the corrected working condition test parameter, obtain the corrected working condition test parameter of each of the 3 electric bicycles produced in this embodiment. Compare the corrected working condition test parameters of the 3 electric bicycles with the preset working condition test parameter threshold respectively. When the corrected working condition test parameters of the 3 electric bicycles are all greater than or equal to the preset working condition test parameter threshold, it indicates that there are no problems with the technology and process for producing electric bicycles set by the manufacturer, and electric bicycles can be directly produced subsequently; when there is a corrected working condition test parameter of the 3 electric bicycles that is less than the preset working condition test parameter threshold, it indicates that there are problems with the technology and process for producing electric bicycles set by the manufacturer, and optimization is required to ensure that the quality of the electric bicycles produced subsequently is qualified. In this embodiment, the preset working condition test parameter threshold is set to 0.7, and the implementer can set the size of the preset working condition test parameter threshold according to the actual situation, which is not limited here.

[0103] So far, the present invention is completed.

[0104] In summary, this embodiment obtains the current data of the battery during the driving process of the electric bicycle after each full charge; according to the change of the current data, obtains the battery deterioration degree and the non-standard driving degree of the electric bicycle; according to the time interval between the end moment of each driving process and the start moment of the electric bicycle charging, and the charging duration difference between each charging of the electric bicycle and its adjacent previous charging, obtains the battery aging degree; according to the battery deterioration degree, the non-standard driving degree and the battery aging degree, corrects the working condition test parameter of the electric bicycle to obtain the corrected working condition test parameter of the electric bicycle. The present invention accurately evaluates the quality of the electric bicycle by accurately obtaining the corrected working condition test parameter, and ensures that the quality of the electric bicycles entering the market is qualified.

[0105] Embodiment 2:

[0106] The present invention also proposes an electric bicycle working condition test system. Please refer to Figure 4 , which shows the structure diagram of an electric bicycle working condition test system provided by an embodiment of the present invention. The system includes: a data acquisition module 10, a battery deterioration degree acquisition module 20, a non-standard driving degree acquisition module 30, a battery aging degree acquisition module 40, and a corrected working condition test parameter acquisition module 50.

[0107] The data acquisition module 10 is used to acquire the current data of the battery at each moment during the driving process of the electric bicycle after each full charge; wherein, the speed during the driving process of the electric bicycle after each full charge is the same, and the driving process after each full charge is a complete driving process.

[0108] The battery deterioration degree acquisition module 20 is configured to obtain the reference battery usage duration for each driving process according to the change of the current data during each driving process; and obtain the battery deterioration degree of the electric bicycle according to the difference in the reference battery usage duration between the last driving process and all other driving processes.

[0109] The non-standard driving degree acquisition module 30 is configured to obtain the non-standard driving degree of the electric bicycle according to the distribution of the current data during each driving process.

[0110] The battery aging degree acquisition module 40 is configured to obtain the battery aging degree of the electric bicycle according to the time interval between the end moment of each driving process and the start moment of charging the electric bicycle, and the difference in the charging duration between each charging of the electric bicycle and the previous adjacent charging.

[0111] The corrected working condition test parameter acquisition module 50 is configured to correct the working condition test parameters of the electric bicycle according to the battery deterioration degree, the non-standard driving degree and the battery aging degree, and obtain the corrected working condition test parameters of the electric bicycle.

[0112] It should be noted that: for the system provided in the above embodiment, only the division of the above functional modules is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, an electric bicycle working condition test system provided in the above embodiment and an embodiment of an electric bicycle working condition test method belong to the same concept. For the specific implementation process, please refer to the method embodiment, which will not be elaborated here.

[0113] Embodiment 3:

[0114] The present invention also provides an electric bicycle working condition test device, which includes a memory and a processor. Among them, the memory stores executable program code, and the processor is configured to call and execute the executable program code to execute an electric bicycle working condition test method provided in an embodiment of the present application. The device may specifically be a chip, a component or a module. The chip may include a connected processor and a memory; among them, the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute an electric bicycle working condition test method provided in the above embodiment.

[0115] In addition, an embodiment of the present application also protects a computer device. Please refer to Figure 5, the computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any one of the electric bicycle working condition testing methods introduced above.

[0116] Embodiment 4:

[0117] This embodiment also provides a computer-readable storage medium. Computer program code is stored in the computer-readable storage medium. When the computer program code runs on a computer, the computer is enabled to execute the above-related method steps to implement an electric bicycle working condition testing method provided by the above embodiment.

[0118] Embodiment 5:

[0119] This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer is enabled to execute the above-related steps to implement an electric bicycle working condition testing method provided by the above embodiment.

[0120] Among them, the device, computer-readable storage medium, computer program product, or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0121] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for testing the working condition of an electric bicycle, characterized in that: The method comprises the following steps: Obtaining a preset number of times of current data of the battery at each moment during the driving process of the electric bicycle after each charging; wherein the speed of the electric bicycle during each driving process after each charging is the same, and each driving process after each charging is a complete driving process; According to the change of current data during each driving process, the reference battery usage time of each driving process is obtained; according to the difference in the reference battery usage time between the last driving process and all other driving processes, the degree of battery degradation of the electric bicycle is obtained; According to the distribution of current data during each driving process, the degree of irregular driving of the electric bicycle is obtained; Obtain the battery aging degree of the electric bicycle according to the time interval between the end of each driving process and the start of charging of the electric bicycle, and the difference in charging time between each charging of the electric bicycle and the previous charging of the electric bicycle; According to the degree of battery degradation, the degree of irregular driving and the degree of battery aging, the operating condition test parameters of the electric bicycle are corrected to obtain the corrected operating condition test parameters of the electric bicycle.

2. The electric bicycle operating condition testing method according to claim 1, characterized in that: The method for obtaining the reference battery usage time is as follows: For any current data in any driving process, a preset window of the current data is constructed, and the power balance coefficient at the time corresponding to the current data is obtained according to the mean and variance of the current data in the preset window; wherein the mean and variance of the current data in the preset window are negatively correlated with the power balance coefficient; Obtain the power balance coefficient at the time corresponding to each current data during the driving process; When the power balance coefficient is greater than the preset power balance coefficient threshold, the corresponding current data are used as the target current data; The duration between the start time of the driving process and the time corresponding to the first appearance of the target current data is obtained as the reference battery usage time of the driving process.

3. The electric bicycle operating condition testing method according to claim 1, characterized in that: The method for obtaining the battery degradation degree is: Obtain the average of the battery reference usage time of all driving processes except the last driving process as the reference time; The result of normalizing the difference between the reference duration and the reference battery usage duration of the last driving process is used as the degree of battery degradation of the electric bicycle.

4. The electric bicycle operating condition testing method according to claim 1, characterized in that: The method for obtaining the irregular driving degree is: For any driving process among the preset number of times, peak current data in the current data of the driving process is determined by peak detection technology; For any current data in the driving process, a reference window of the current data is constructed, and a fluctuation anomaly coefficient of the current data is obtained according to the change rate between the maximum current data and the minimum current data in the reference window and the fluctuation degree of the peak current data; Obtaining a fluctuation anomaly coefficient of each current data during the driving process, and taking the current data corresponding to the fluctuation anomaly coefficient greater than a preset fluctuation anomaly threshold as abnormal current data; According to the distribution of abnormal current data during the driving process, the degree of random driving during the driving process is obtained; The random driving degrees of all driving processes in a preset number of times are accumulated and normalized to obtain a result which is used as the irregular driving degree of the electric bicycle.

5. The electric bicycle operating condition testing method according to claim 4, characterized in that: The method for obtaining the fluctuation anomaly coefficient is: For any current data in any driving process, obtaining a difference between the maximum current data and the minimum current data in a reference window of the current data as a first difference; Acquire the duration between the moments corresponding to the maximum current data and the minimum current data in the reference window of the current data as the first duration; Using the ratio of the first difference to the first duration as the current reference change rate; Obtain the variance of the peak current data in the reference window of the current data as a waveform reference value; The result of adding the current reference change rate and the waveform reference value and then normalizing them is used as the fluctuation anomaly coefficient of the current data.

6. The electric bicycle operating condition testing method according to claim 4, characterized in that: The method for obtaining the degree of random driving is: For any driving process, the area formed by the continuous and uninterrupted abnormal current data in the driving process is regarded as the abnormal fluctuation area; Obtaining the time interval between any two adjacent abnormal fluctuation areas as the second time interval; According to the average value of all the second time periods during the driving process and the proportion of abnormal current data in the driving process, the degree of random driving during the driving process is obtained; wherein, the average value of the second time period is negatively correlated with the degree of random driving, and the proportion of abnormal current data in the driving process is positively correlated with the degree of random driving.

7. The electric bicycle operating condition testing method according to claim 1, characterized in that: The method for obtaining the battery aging degree is: The complete charging and discharging process of the electric bicycle is regarded as one charging and discharging process; Obtaining the time interval between two adjacent charging and discharging processes as the third time interval; The result of adding up all the third durations and performing normalization is used as the first aging reference value; Obtain the difference between the charging time of each charging of the electric bicycle for a preset number of times and the charging time of the previous charging, and use them as the first difference; The result of adding all the first difference values ​​and performing normalization is used as the second aging reference value; The product of the first aging reference value and the second aging reference value is used as the aging degree of the battery of the electric bicycle.

8. The electric bicycle operating condition testing method according to claim 1, characterized in that: The method for obtaining the modified working condition test parameters is as follows: Obtaining a correction weight according to the degree of battery degradation, the degree of irregular driving and the degree of battery aging; wherein the degree of battery degradation, the degree of irregular driving and the degree of battery aging are all negatively correlated with the correction weight; The product of the correction weight and the operating condition test parameter is used as the correction operating condition test parameter.

9. The electric bicycle operating condition testing method according to claim 1, characterized in that: The method for obtaining the working condition test parameters is: The ratio of the output power to the input power of the electric bicycle battery is used as the working condition test parameter of the electric bicycle.

10. The electric bicycle operating condition testing method according to claim 1, characterized in that: The preset number is set to 50.

Images