Electric bicycle working condition testing method

Abstract

The invention relates to the technical field of battery measurement, in particular to an electric bicycle working condition testing method. The method comprises the following steps: acquiring current data of a battery in a running process after the electric bicycle is charged every time; according to the change condition of the current data, the battery degradation degree and the non-standard driving degree of the electric bicycle are obtained; acquiring the aging degree of the battery according to the time interval between the ending moment of each running process and the charging starting moment of the electric bicycle and the charging duration difference between each charging of the electric bicycle and the adjacent previous charging; according to the battery degradation degree, the non-standard driving degree and the battery aging degree, the working condition test parameters of the electric bicycle are corrected, and the corrected working condition test parameters of the electric bicycle are obtained. According to the method, the quality of the electric bicycle is accurately evaluated by accurately acquiring the corrected working condition test parameters, so that the quality of the electric bicycle in the market is ensured to be qualified.

Description

Technical Field

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

[0002] With the aggravation of urban traffic congestion and the improvement of environmental awareness, electric bicycles are becoming more and more popular as a green travel mode, and their importance in urban transportation is becoming more and more significant. In order to meet users' demand for electric bicycles, electric bicycle manufacturers need to conduct working condition tests on electric bicycles before they leave the factory to ensure the operating quality of electric bicycles, thereby promoting the healthy development of the electric bicycle industry. It is known that the power of electric bicycles is batteries, so the working condition of electric bicycles can be reflected by measuring the batteries of electric bicycles.

[0003] In the existing method, the difference between the output power and input power of the electric bicycle battery is analyzed to standardize the evaluation of the working condition of the electric bicycle. However, in reality, with the increase of the running time of the electric bicycle and the bad behavior such as irregular riding of the electric bicycle, the battery of the electric bicycle will deteriorate to a certain extent, resulting in deviations in the actual discharge current of the electric bicycle battery, which in turn leads to inaccurate standardized evaluation of the working condition of the electric bicycle, and thus it is impossible to accurately analyze the running quality of the electric bicycle. Summary of the invention

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

[0005] An embodiment of the present invention provides an electric bicycle operating condition testing method, the method comprising the following steps:

[0006] 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;

[0007] 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;

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

[0009] 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;

[0010] 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.

[0011] Furthermore, the method for obtaining the reference battery usage time is:

[0012] 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;

[0013] Obtain the power balance coefficient at the time corresponding to each current data during the driving process;

[0014] 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;

[0015] 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.

[0016] Furthermore, the method for obtaining the battery degradation degree is:

[0017] Obtain the average of the battery reference usage time of all driving processes except the last driving process as the reference time;

[0018] 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.

[0019] Furthermore, the method for obtaining the degree of irregular driving is:

[0020] 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;

[0021] 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;

[0022] 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;

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

[0024] 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.

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

[0026] 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;

[0027] 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;

[0028] Using 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 the current data as a waveform reference value;

[0030] 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.

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

[0032] 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;

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

[0034] 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.

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

[0036] The complete charging and discharging process of the electric bicycle is regarded as one charging and discharging process;

[0037] Obtaining the time interval between two adjacent charging and discharging processes as the third time interval;

[0038] The result of adding up all the third durations and performing normalization is used as the first aging reference value;

[0039] 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;

[0040] The result of adding all the first difference values ​​and performing normalization is used as the second aging reference value;

[0041] 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.

[0042] Furthermore, the method for obtaining the modified working condition test parameters is:

[0043] 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;

[0044] The product of the correction weight and the operating condition test parameter is used as the correction operating condition test parameter.

[0045] Furthermore, the method for obtaining the working condition test parameters is:

[0046] 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.

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

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

[0049] The present invention obtains the reference battery usage time of each driving process according to the change of current data during each driving process, and accurately reflects the actual discharge time 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 degradation degree of the electric bicycle is obtained according to the difference in the reference battery usage time between the last driving process and all other driving processes, and accurately reflects the damage degree of the battery caused by the battery degradation, which is conducive to the subsequent accurate adjustment of the working condition test parameters of the electric bicycle; further, according to the distribution of current data during each driving process, the irregular driving degree of the electric bicycle is obtained, and the impact of the irregular driving of the electric bicycle is accurately reflected, which is also conducive to the subsequent accurate adjustment of the working condition test parameters of the electric bicycle. Subsequently, the working condition test parameters of the electric bicycle are accurately adjusted; further, based on the time interval between the end of each driving process and the start of charging of the electric bicycle, as well as the difference in charging time between each charging of the electric bicycle and the previous adjacent charging, the degree of battery aging of the electric bicycle is obtained, accurately reflecting the degree of damage to the battery due to battery aging, which is also conducive to the subsequent accurate adjustment of the working condition test parameters of the electric bicycle; therefore, the working condition test parameters of the electric bicycle are corrected according to the degree of battery degradation, the degree of irregular driving and the degree of battery aging, and the corrected working condition test parameters of the electric bicycle are accurately obtained, so that the quality of the electric bicycle can be accurately evaluated to avoid the production of electric bicycles with unqualified quality, 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 drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A schematic flow chart of an electric bicycle operating condition testing method provided by one embodiment of the present invention;

[0052] Figure 2 A flow chart of a method for obtaining the degree of irregular driving provided by one embodiment of the present invention;

[0053] Figure 3 A flow chart of a method for obtaining test parameters of a modified working condition provided by an embodiment of the present invention;

[0054] Figure 4 A structural diagram of an electric bicycle operating condition test system provided by one embodiment of the present invention;

[0055] Figure 5A schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of an electric bicycle working condition test method proposed by the present invention, its specific implementation method, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0057] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0058] The specific scheme of the electric bicycle working condition testing method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0059] Embodiment 1:

[0060] The specific scenario of this embodiment is: in order to ensure that the quality of the electric bicycles on the market is qualified, the manufacturer of electric bicycles first produces several electric bicycles for working condition detection before producing the same batch of electric bicycles, analyzes the quality of the electric bicycles, and ensures that the quality of the electric bicycles produced subsequently is qualified. In order to accurately detect the working condition of the electric bicycles to be analyzed, this embodiment analyzes each electric bicycle to be analyzed, first obtains the current data of the battery at each moment during the driving process of each electric bicycle to be analyzed after charging for a preset number of times; then analyzes the current data during each driving process, obtains the battery degradation degree and irregular driving degree, and battery aging degree of each electric bicycle to be analyzed; finally, according to the battery degradation degree, irregular driving degree and battery aging degree, the working condition test parameters of each electric bicycle to be analyzed are corrected, and the corrected working condition test parameters of each electric bicycle to be analyzed are obtained, and the quality of each electric bicycle to be analyzed is accurately analyzed, which indirectly reflects the quality of other electric bicycles in the same batch produced later, and ensures that the quality of electric bicycles entering the market meets the standards.

[0061] This embodiment proposes a method for testing the working condition of an electric bicycle. Figure 1 , which shows a schematic flow chart of an electric bicycle operating condition testing method provided by an embodiment of the present invention, the method comprising the following steps:

[0062] Step S1: obtaining the current data of the battery at each moment during the driving process of the electric bicycle after each charging for a preset number of times; 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.

[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 electric bicycle manufacturer first produces three electric bicycles using the same production technology and process, and then analyzes the working conditions of the three electric bicycles to intermittently reflect the quality of the electric bicycles subsequently produced by the electric bicycle manufacturer. The implementer can set the number of electric bicycles produced by the electric bicycle manufacturer using the same production technology and process according to actual conditions, and it is not limited here.

[0064] In order to make a clear analysis, this embodiment takes any one of the three electric bicycles as an example for analysis, and the electric bicycles that appear later are all the selected electric bicycles. It is known that with the increase in the use time of electric bicycles and the bad behaviors such as irregular riding of electric bicycles, the battery of the electric bicycle will deteriorate to a certain extent, resulting in deviations in the actual discharge current of the electric bicycle battery, which in turn leads to inaccurate standardized evaluation of the working condition of the electric bicycle, and thus it is impossible to accurately analyze the running quality of the electric bicycle. In order to accurately evaluate the working condition of the electric bicycle, this embodiment needs to analyze the damage of the electric bicycle battery. Therefore, this embodiment obtains the current data of the battery at each moment during the driving process of the electric bicycle after each charging of the preset number of times through the Hall effect current sensor. This embodiment sets the preset number to 50, and sets the data sampling frequency of the Hall effect current sensor 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. Among them, the speed of the electric bicycle during the driving process after each charging is the same, and the driving process after each charging is a complete driving process. At the same time, the road conditions on which electric bicycles travel each time are random. Electric bicycles can be ridden on various road conditions such as flat roads, uphill roads, highways and mountain roads.

[0065] In order to improve the accuracy of analyzing the battery damage through current data, this embodiment performs pre-processing operations such as denoising and calibration on the current data obtained during each driving process to ensure the accuracy of the collected data.

[0066] Step S2: 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.

[0067] Specifically, during the driving process of the electric bicycle, when the current data gradually decreases and tends to be stable, it means that the battery has reached a balance of power, that is, the current discharge has ended. Then, this embodiment obtains the reference battery usage time of each driving process according to the change of the current data during each driving process. It is known that the loss of the battery gradually increases with the increase of the use time. Therefore, the greater the difference in the reference battery usage time between the last driving process of the electric bicycle and all other driving processes in the preset number of times, the more serious the damage to the battery of the electric bicycle. Then, this embodiment obtains the degree of battery degradation of the electric bicycle according to the difference in the reference battery usage time between the last driving process and all other driving processes. The greater the degree of battery degradation, the greater the degree of loss of the battery of the electric bicycle after the preset number of driving times.

[0068] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the reference battery usage time is: for any current data in any driving process, a preset window for the current data is constructed. This embodiment takes the current data as the end point, and constructs the 19 current data that are located before the current data in time sequence and closest to the current data in time sequence and the current data as the preset window for the current data. For example, taking the i-th current data as an example, the area corresponding to the i-19th current data to the i-th current data is the preset window for the i-th current data. The implementer can construct the preset window for each current data according to the actual situation, and it is not limited here. It should be noted that for boundary current data in time sequence, if there are less than 19 current data before a certain boundary current data, the current data that exists before the boundary current data and the boundary current data are constructed as the preset window for the boundary current data;

[0069] When the current data in the preset window of the current data is smaller and more stable, the time corresponding to the current data is more likely to be the battery charge balance time, and then the charge 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 charge balance coefficient; the larger the charge balance coefficient, the more likely the time corresponding to the current data is the battery charge balance time. wherein the calculation formula of the charge balance coefficient is: B i =exp(-(μ i +S i )); where B i is the power balance coefficient of the i-th current data; μ i is the mean value 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 an exponential function with a natural constant as the base;

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

[0071] At this point, the reference battery usage time of each driving process of the electric bicycle in the preset number of times is obtained.

[0072] Preferably, in one achievable manner of this embodiment, the method for obtaining the battery degradation degree is:

[0073] The average of the battery reference usage time of all driving processes except the last driving process in the preset number of times is obtained as the reference time; when the difference between the reference time and the battery reference usage time of the last driving process is larger, it means that the electric bicycle is more likely to be affected by battery aging, and then the difference between the reference time and the battery reference usage time of the last driving process is obtained and normalized as the battery degradation degree of the electric bicycle. The calculation formula of the battery degradation degree is: D = norm (l ′ -l last );where D is the degree of battery degradation; l ′ is the reference duration; last is the reference battery usage time of the last driving process; norm is the normalization function.

[0074] At this point, the degree of battery degradation of the electric bicycle is obtained.

[0075] Step S3: Obtain the degree of irregular driving 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 in any driving process, this embodiment takes the current data as the end point, and constructs the 20 current data that are located before the current data in time sequence and closest to the current data in time sequence and the current data as the reference window of the current data. For example, taking the i-th current data as an example, the area corresponding to the i-20th 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 the current data according to the actual situation, which is not limited here. It should be noted that for the boundary current data in time sequence, if there are less than 20 current data before a certain boundary current data, the current data existing before the boundary current data and the boundary current data are constructed as the reference window of the boundary current data. It is known that the current data fluctuation caused by irregular driving is 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 means that the current data is more likely to be caused by abnormal driving behavior. Furthermore, this embodiment obtains the fluctuation anomaly coefficient of the corresponding current data 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 larger the fluctuation anomaly coefficient, the more likely the corresponding current data has fluctuation anomaly.

[0082] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the fluctuation anomaly coefficient is: for any current data in any driving process, the absolute value of the difference between the maximum current data and the minimum current data in the reference window of the current data is obtained as the first difference; the duration between the corresponding moments of the maximum current data and the minimum current data in the reference window of the current data is obtained as the first duration; if there is more than one maximum current data or minimum current data in the reference window of the current data, the duration between each maximum current data and each minimum current data is obtained, and then the minimum duration is used as the first duration; the ratio of the first difference to the first duration is used 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 the current data is to have fluctuation anomalies. In order to further analyze the degree of fluctuation anomalies in the current data, the variance of the peak current data in the reference window of the current data is obtained 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 the current data, which indirectly indicates that the current data is more likely to have fluctuation anomalies. Then, the result of adding the current reference change rate and the waveform reference value and normalizing them is used as the fluctuation anomaly coefficient of the current data. The calculation formula of the fluctuation anomaly coefficient is: Where Y j is the fluctuation anomaly coefficient of the jth current data; H j,max is the maximum current data in the reference window of the jth current data; Hj,min is the minimum current data in the reference window of the jth 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 abnormality coefficient of each current data during the driving process, and take the current data corresponding to the fluctuation abnormality coefficient greater than the preset fluctuation abnormality 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. Therefore, this embodiment obtains the fluctuation anomaly coefficient of each current data during a certain driving process, and uses the current data corresponding to the fluctuation anomaly coefficient greater than the preset fluctuation anomaly threshold as the abnormal current data during the driving process. This embodiment sets the preset fluctuation anomaly threshold to 0.6, and the implementer can set the size of the preset fluctuation anomaly threshold according to actual conditions, which is not limited here.

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

[0086] When there are more abnormal current data in a certain driving process and the time interval between the areas corresponding to the abnormal current data is smaller, it means that irregular driving behaviors frequently occur in the driving process, which indirectly means that the degree of random driving in the driving process is greater. Therefore, this embodiment obtains the degree of random driving in the driving process according to the distribution of abnormal current data in a certain driving process.

[0087] Preferably, in a method that can be implemented in this embodiment, 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 taken as the abnormal fluctuation area; when the time interval between two adjacent abnormal fluctuation areas is shorter, it means that the irregular driving behavior occurs more frequently; then the time interval between any two adjacent abnormal fluctuation areas is obtained as the second time interval; the smaller the second time interval, the more frequent the irregular driving behavior occurs. Wherein, corresponding to any two adjacent abnormal fluctuation areas, the time interval between the end time of the previous abnormal fluctuation area and the start time of the next abnormal fluctuation area is taken as the second time interval. In order to accurately represent the degree of random driving in the driving process, the degree of random driving in the driving process is obtained according to the mean of all the second time intervals in the driving process and the proportion of abnormal current data in the driving process; wherein, the mean of the second time interval 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. The greater the degree of random driving, the more irregular driving behaviors in the driving process, and the greater the damage to the electric bicycle battery. Wherein, the calculation formula of the degree of random driving is: In the formula, R a is the degree of random driving during the a-th driving process; is the average of all second durations during the a-th driving process; α is the first preset constant, which is 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, which is not limited here.

[0089] Step S205: Accumulate and normalize the random driving degrees of all driving processes in a preset number of times to obtain a result as the irregular driving degree of the electric bicycle.

[0090] The influence of the irregular driving of an electric bicycle on the battery is irreversible. In order to accurately determine the irregular driving degree of the electric bicycle, this embodiment accumulates the random driving degrees of all driving processes in a preset number of times and normalizes the result as the irregular driving degree of the electric bicycle. This embodiment normalizes the sum of the random driving degrees of all driving processes in a preset number of times 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 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.

[0092] In actual situations, after each driving of an electric bicycle ends, that is, after the battery of the electric bicycle is discharged, if the electric bicycle is not charged in time, the battery may be deeply discharged, which will cause further aging of the battery. It is known that when the battery of an electric bicycle ages, the charging time of the electric bicycle will increase. In addition, this embodiment obtains the degree of battery aging 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, as well as the difference in charging time between each charging of the electric bicycle and the previous charging of the electric bicycle. The greater the degree of battery aging, the greater the degree of damage to the battery of the electric bicycle. This embodiment directly obtains the charging time of each charging of the electric bicycle through the charging system. Among them, the end time of each driving process of the electric bicycle is the time when the battery no longer generates current data.

[0093] Preferably, in a manner that can be implemented in this embodiment, the method for obtaining the degree of battery aging is as follows: for the convenience of description, this embodiment regards a complete charging and discharging process of an electric bicycle as one charging and discharging process; obtains the duration of the time interval between two adjacent charging and discharging processes as the third duration; wherein, the duration between the end time of the previous charging and discharging process and the start time of the next charging and discharging process in two adjacent charging and discharging 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 degree of aging of the battery of the electric bicycle, this embodiment adds up all the third durations in a preset number of charging and discharging processes and normalizes them. The result is used as the first aging reference value; the larger the first aging reference value is, the greater the degree of aging of the battery of the electric bicycle is likely to be; in order to more accurately obtain the degree of aging of the battery of the electric bicycle, 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 is obtained, and all are used as the first difference; the larger the first difference is, the greater the degree of battery aging is; in order to analyze the degree of battery aging as a whole, all the first differences are added and normalized as the result, which is used as the second aging reference value; the larger the second aging reference value is, the greater the degree of aging of the battery of the electric bicycle is; and the product of the first aging reference value and the second aging reference value is used as the degree of aging of the battery of the electric bicycle. Wherein, the value range of the battery aging degree is between 0 and 1.

[0094] Step S5: 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.

[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 input power of the electric bicycle battery are both known, and the working condition test parameters obtained by the existing method do not take into account the damage to the battery itself, which leads to the working condition test parameters not being able to accurately reflect the quality of the electric bicycle. It is known that the greater the degree of battery degradation, the greater the degree of irregular driving and the greater the degree of battery aging, the greater the damage to the battery of the electric bicycle, which indirectly indicates that the possibility of deviation in the current of the electric bicycle is greater. Therefore, this embodiment corrects the working condition test parameters of the electric bicycle according to the degree of battery degradation, the degree of irregular driving and the degree of battery aging, obtains the corrected working condition test parameters of the electric bicycle, and then accurately reflects the quality of the electric bicycle, and indirectly infers whether the technology and process for producing electric bicycles need to be optimized.

[0096] Preferably, in one possible implementation of this embodiment, the method for obtaining the modified working condition test parameters can be found in Figure 3 , which shows a flow chart of a method for obtaining a modified working condition test parameter provided in this embodiment, the method comprising the following steps:

[0097] Step S301: obtaining a correction weight according to the battery degradation degree, the irregular driving degree and the battery aging degree; wherein the battery degradation degree, the irregular driving degree and the battery aging degree are all negatively correlated with the correction weight.

[0098] It is known that the greater the degree of battery degradation, the greater the degree of irregular driving and the greater the degree of battery aging, the greater the possibility of deviation of the electric bicycle's current, and the smaller the working condition test parameter should be, indicating that the quality of the electric bicycle is less qualified. Furthermore, this embodiment obtains the 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. Among them, the calculation formula of the correction weight is: In the formula, is the correction weight; D is the degree of battery degradation; 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 larger the norm(D+G+X), the greater the possibility of current deviation in the electric bicycle, which indirectly indicates that the operating condition test parameters are more inaccurate and the operating condition test parameters should be smaller, and then the correction weight of the operating condition test parameters is obtained through 1-norm(D+G+X).

[0100] Step S302: multiplying the product of the correction weight and the operating condition test parameter as the correction operating condition test parameter.

[0101] The smaller the modified operating condition test parameter is, the more likely it is that there is a problem with the quality of the electric bicycle.

[0102] According to the method for obtaining the corrected working condition test parameters, the corrected working condition test parameters of each of the three electric bicycles produced in this embodiment are obtained. The corrected working condition test parameters of the three electric bicycles are respectively compared with the preset working condition test parameter thresholds. When the corrected working condition test parameters of the three electric bicycles are all greater than or equal to the preset working condition test parameter thresholds, it means 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 the corrected working condition test parameters of the three electric bicycles are less than the preset working condition test parameter thresholds, it means that there are problems with the technology and process for producing electric bicycles set by the manufacturer, and optimization is required to ensure the quality of the electric bicycles produced subsequently. In this embodiment, the preset working condition test parameter threshold is set to 0.7. The implementer can set the size of the preset working condition test parameter threshold according to the actual situation, and it is not limited here.

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

[0104] In summary, this embodiment obtains the battery current data of the electric bicycle during each driving process after the electric bicycle is fully charged; obtains the battery degradation degree and irregular driving degree of the electric bicycle according to the change of the current data; obtains the battery aging degree according to the time interval between the end of each driving process and the start of the charging of the electric bicycle, and the difference in charging time between each charging of the electric bicycle and the previous charging; corrects the working condition test parameters of the electric bicycle according to the battery degradation degree, irregular driving degree and battery aging degree, and obtains 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, thereby ensuring that the quality of the electric bicycles on the market is qualified.

[0105] Embodiment 2:

[0106] The present invention also proposes an electric bicycle operating condition test system, please refer to Figure 4 , which shows a structural diagram of an electric bicycle operating condition test system provided by an embodiment of the present invention, the system includes: a data acquisition module 10, a battery degradation degree acquisition module 20, an irregular driving degree acquisition module 30, a battery aging degree acquisition module 40 and a corrected operating condition test parameter acquisition module 50.

[0107] The data acquisition module 10 is used to obtain the current data of the battery at each moment during the driving process of the electric bicycle after each charging for a preset number of times; 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.

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

[0109] The irregular driving degree acquisition module 30 is used to acquire the irregular 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 used to acquire 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.

[0111] The corrected operating condition test parameter acquisition module 50 is used to correct the operating condition test parameters of the electric bicycle according to the degree of battery degradation, the degree of irregular driving and the degree of battery aging, and obtain the corrected operating condition test parameters of the electric bicycle.

[0112] It should be noted that: the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, 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, the electric bicycle working condition test system and the electric bicycle working condition test method embodiment provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0113] Embodiment 3:

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

[0115] In addition, the present application embodiment also protects a computer device, see Figure 5The 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, wherein when the processor 402 executes the computer program 403, the computer device can execute any one of the electric bicycle operating condition test methods introduced above.

[0116] Embodiment 4:

[0117] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement an electric bicycle operating condition testing method provided in the above embodiment.

[0118] Embodiment 5:

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

[0120] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment is 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 repeated here.

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

[0122] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced 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.

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