Battery health determination method and device, equipment and storage medium

Abstract

The invention discloses a battery health determination method, device and equipment and a storage medium, and relates to the technical field of vehicle management, and the method comprises the steps: carrying out the charging segment screening of a battery charging log of a target vehicle-mounted battery, obtaining an effective charging segment, collecting the battery charging log based on the number of continuous sampling days and the number of collection times, and obtaining an effective charging segment; the number of continuous sampling days is the number of days of continuous log sampling, and the number of collection times is the number of times of continuous sampling execution; performing health degree calculation according to the effective charging segments to obtain the battery health degree corresponding to each effective charging segment; and weighting the battery health degree corresponding to each effective charging segment, and determining the battery health degree corresponding to the target vehicle-mounted battery. The battery health degree is monitored on the basis of the continuous sampling days and the collection times, so that the battery health degree can be monitored without collecting all data, most vehicles can be covered, and the battery health degrees of different groups and / or different types of power batteries can be monitored at the cloud.

Description

Technical Field

[0001] This application relates to the field of vehicle management technology, and in particular to methods, apparatus, devices and storage media for determining battery health. Background Technology

[0002] With the rapid development of new energy vehicles, the application of vehicle power batteries has become increasingly widespread. However, vehicle power batteries will experience battery life issues as they are used. When the battery health (SOH) degrades to around 70%, it is considered that the battery life has been terminated. Relevant departments, car manufacturers, and power battery manufacturers need to monitor the health of power batteries in vehicles (different groups and / or different types of power batteries, including important customers, designated latitude and longitude areas, production batches, ternary lithium or phosphorus iron batteries) to ensure that when the battery health is low, they can both issue alerts to users and monitor the quality. Based on this, how to monitor the battery health of vehicles produced by car manufacturers in the cloud has become an urgent problem to be solved by relevant departments, car manufacturers, and power battery manufacturers. Summary of the Invention

[0003] The main purpose of this application is to provide a method, apparatus, device and storage medium for determining battery health, which aims to solve the technical problem that it is difficult for relevant technical departments, car manufacturers and power battery manufacturers to monitor the health of on-board power batteries in vehicles produced by car manufacturers in the cloud.

[0004] To achieve the above objectives, this application proposes a method for determining battery health, the method comprising: The battery charging logs of the target vehicle battery are filtered to obtain valid charging segments. The battery charging logs are collected based on the number of consecutive sampling days and the number of collections. The number of consecutive sampling days is the number of days in the continuous sampling logs, and the number of collections is the number of times continuous sampling is performed. The health status of the battery is calculated based on the effective charging segments to obtain the battery health status corresponding to each effective charging segment. The battery health of the target vehicle battery is determined by weighting the battery health of each effective charging segment.

[0005] Optionally, before filtering the charging segments of the target vehicle battery's battery charging log to obtain valid charging segments, the method further includes: Obtain the number of consecutive sampling days and the number of sampling times; The battery operation log of the target vehicle battery is collected based on the number of consecutive sampling days and the number of sampling times; The battery operation log is filtered to obtain the battery charging log.

[0006] Optionally, obtaining the number of consecutive sampling days and the number of sampling times includes: Obtain the vehicle application type corresponding to the target vehicle battery; The average daily mileage and average daily coverage of the target vehicle battery are determined based on the vehicle application type. The average daily mileage is the average daily mileage of the vehicle corresponding to the target vehicle battery, and the average daily coverage is the probability of collecting effective charging segments on a single day. The number of consecutive sampling days is determined based on the full-charge range of the target vehicle battery and the average daily mileage. The number of sampling times is determined based on the number of consecutive sampling days and the average daily coverage.

[0007] Optionally, determining the number of sampling times based on the number of consecutive sampling days and the average daily coverage includes: Set the number of tests to be performed to the minimum value of the number of tests; The sampling coverage rate is calculated based on the number of tests to be performed, the number of consecutive sampling days, and the average daily coverage rate. If the sampling coverage rate is greater than or equal to a preset coverage threshold, then the number of times to be detected is taken as the number of times to collect samples; or, If the sampling coverage rate is less than the preset coverage rate threshold, the number of tests to be performed is increased to generate a new number of tests to be performed, and the process of calculating the sampling coverage rate based on the number of tests to be performed, the number of consecutive sampling days, and the average daily coverage rate is returned.

[0008] Optionally, the step of filtering charging segments from the battery charging logs of the target vehicle battery to obtain valid charging segments includes: The battery charging log of the target vehicle battery is divided into segments to generate at least one charging segment; Obtain the battery type of the target vehicle battery; If the battery type is the first type, then the abrupt change segment at the edge is determined according to the battery charging curve corresponding to the target vehicle battery. The first type of battery is a battery with a non-smooth charging curve. Based on the edge-change segment, the battery charging log of the target vehicle battery is filtered to obtain valid charging segments.

[0009] Optionally, after obtaining the battery type of the target vehicle battery, the method further includes: If the battery type is the second type, then the estimation error corresponding to each charging segment is obtained. The second type of battery is a battery with a smooth charging curve. Charging segments with an estimated error less than or equal to a preset error threshold are considered valid charging segments.

[0010] Optionally, the step of filtering charging segments from the battery charging logs of the target vehicle battery to obtain valid charging segments includes: Anomaly identification is performed on the battery charging logs of the target vehicle battery to determine the abnormal logs; Remove the abnormal logs from the battery charging logs to identify valid charging logs; The valid charging logs are filtered to obtain valid charging segments; Anomaly detection is based on at least one of the following: State of charge continuity; The rationality of the current direction and magnitude; SOC monotonicity and non-decreasingness; Vehicle speed and gear selection are consistent; Matching of charging time with changes in SOC.

[0011] Furthermore, to achieve the above objectives, this application also provides a battery health determination device, the battery health determination device comprising: The filtering module is used to filter the charging segments of the battery charging log of the target vehicle battery to obtain valid charging segments. The battery charging log is collected based on the number of consecutive sampling days and the number of collections. The number of consecutive sampling days is the number of days of the continuous sampling log, and the number of collections is the number of times continuous sampling is performed. The calculation module is used to calculate the battery health based on the effective charging segments and obtain the battery health corresponding to each effective charging segment. The weighting module is used to weight the battery health of each valid charging segment to determine the battery health of the target vehicle battery.

[0012] In addition, to achieve the above objectives, this application also provides a battery health determination device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the battery health determination method as described above.

[0013] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the battery health determination method as described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the battery health determination method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: Because battery health monitoring is based on the number of consecutive sampling days and the number of sampling times, it ensures that battery health monitoring can be achieved without collecting all the data, and it can cover most vehicles. Therefore, it ensures that the battery health of vehicles can be monitored in the cloud. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating Embodiment 1 of the battery health determination method of this application; Figure 2 This is a flowchart illustrating Embodiment 2 of the battery health determination method of this application. Figure 3 This is a flowchart illustrating Embodiment 3 of the battery health determination method of this application; Figure 4 This is a schematic diagram of the module structure of the battery health determination device according to an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the battery health determination method in this application embodiment.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] Based on this, embodiments of this application provide a method for determining battery health, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the battery health determination method of this application.

[0023] In this embodiment, the battery health determination method includes steps S10 to S30: Step S10: Filter the charging segments of the battery charging log of the target vehicle battery to obtain valid charging segments. The battery charging log is collected based on the number of consecutive sampling days and the number of collections. The number of consecutive sampling days is the number of days in the continuous sampling log, and the number of collections is the number of times continuous sampling is performed.

[0024] It should be noted that the execution subject of this embodiment may be a battery health determination device or a platform composed of at least one battery health determination device. The battery health determination device may be an electronic device such as a smart computer, server or cloud server, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment and the following embodiments, the battery health determination method of this application is described using a battery health determination device as an example.

[0025] It should be noted that the target vehicle battery can be the power battery in a new energy vehicle that requires cloud-based monitoring of battery health. A valid charging segment can be a charging log segment that can be used normally to calculate battery health.

[0026] In practical applications, collecting battery charging logs based on the number of consecutive sampling days and the number of collections can enable the coverage rate of battery health calculation based on the battery charging logs to reach a preset coverage rate threshold. Here, the coverage rate can be the proportion of the target vehicle battery with corresponding effective charging segments among all target vehicle batteries. The number of consecutive sampling days and the number of collections can be pre-calibrated by the administrator of the battery health determination device according to the actual needs of the preset coverage rate threshold.

[0027] For example, assuming the preset coverage threshold is 80%, it means that after testing, at least 80% of the target vehicle batteries should be able to be calculated normally for battery health. Based on this, the battery health determination equipment administrators calibrate the system and set the continuous sampling days to 3 and the number of sampling times to 3.

[0028] In practical use, if automakers and other organizations need to monitor the health of the power batteries in vehicles and ensure that they can alert users when the battery health is low, they need to monitor the batteries in the large number of vehicles they produce. Generally, automakers produce and put into use tens of thousands, hundreds of thousands, or even more vehicles. In this case, if battery data is collected from vehicles at a fixed frequency, the amount of battery data will be enormous. For example, if the current conventional battery monitoring frequency is to collect battery data once every 10 seconds, then a single vehicle will generate 6*60*24=8640 data entries per day. Even if only 50,000 vehicles are managed, the total amount of data per day will still be 8640*50000=432,000,000. This situation leads to an enormous amount of data that car manufacturers and other organizations need to process, consuming a huge amount of resources, and the scale of the data to be processed is too large (e.g., if the total data volume is n, then the data volume that needs to be processed is generally O(n^2)). 2 From O(n) 3 (between ) will also lead to excessive processing resources required for actual computing, excessive time consumption, and high overall cost, which the cloud can hardly bear.

[0029] To address this issue, the number of consecutive sampling days and the number of collections can be preset. During a comprehensive monitoring cycle, instead of collecting battery charging logs consistently, the battery charging logs of the target vehicle battery are collected based on the number of consecutive sampling days and the number of collections, in order to minimize the amount of data that needs to be processed.

[0030] For example, assuming a comprehensive monitoring cycle of one month, with the continuous sampling days set to 3 and the number of collections set to 3, only 3 collections are needed within a month, each collecting the battery charging logs of the target vehicle battery for 3 consecutive days (e.g., three days at the beginning, three days in the middle, and three days at the end of the month). Therefore, compared to continuous collection, the amount of data required is only 30%, significantly reducing the amount of data collected, and the computational processing volume is only 9% (30%*30%) to 2.7% (30%*30%*30%). Furthermore, battery degradation is slow, and with longer time intervals allowed, the amount of data required can be further reduced. For example, collecting data once a year would only require 2.5% of the data, and the computational processing volume would only be 0.0625% (2.5%*2.5%) to 0.0015625% (2.5%*2.5%*2.5%).

[0031] Step S20: Calculate the battery health based on the effective charging segments to obtain the battery health corresponding to each effective charging segment.

[0032] In practical use, the high current during fast charging can interfere with the accuracy of voltage readings, potentially leading to inaccurate battery health calculations. Calculating battery health based on voltage requires waiting for the battery to settle after fast charging to ensure accuracy, which is unsuitable for this scenario. Therefore, to guarantee the accuracy and effectiveness of the calculation, current can be used to determine battery health. The specific calculation formula is shown below:

[0033] In the formula, SOH represents the battery health corresponding to the effective charging segment; I(t) can be the charging current (unit: A), which is negative during charging, but the integral takes its absolute value or is treated according to convention; C 标称 The nominal capacity (in Ah) of the target vehicle battery, such as 100 Ah; SOC endThe SOC (State of Charge) is the battery capacity of the target on-board battery at the end of an effective charging segment. st t represents the battery capacity of the target onboard battery at the start of an effective charging segment. end t is the end time of the effective charging segment. st This is the start time of a valid charging segment.

[0034] In practical use, the target vehicle battery may correspond to one or more effective charging segments. Based on this, the battery health can be calculated for each effective charging segment using the above method, thereby obtaining the battery health corresponding to each effective charging segment.

[0035] Step S30: Weight the battery health of each effective charging segment to determine the battery health of the target vehicle battery.

[0036] In practical use, there may be some errors in the calculation of battery health. Therefore, in order to ensure the accuracy of battery health as much as possible, the battery health corresponding to each effective charging segment can be weighted, and the weighted result can be used as the battery health corresponding to the target vehicle battery to reduce errors.

[0037] For example, the calculation of battery health for each effective charging segment has an error of ±3%, while weighted averaging can reduce the variance. After weighting, the error may only be ±1%, thereby reducing the error and improving the accuracy of battery health.

[0038] In practical use, the greater the change in battery charge within an effective charging segment, the more reliable the calculated battery health is generally. Therefore, the weights used in the weighted average can be set based on the change in battery charge corresponding to the effective charging segment. Specifically, the change in battery charge within an effective charging segment, ΔSOC, is equal to the SOC value. end -SOC st .

[0039] For example, the weights for weighted calculations can be:

[0040] Therefore, the final calculated battery health status of the target vehicle battery can be:

[0041] In the formula, the wth i The weighting of the i-th valid charging segment is ΔSOC. i Let ΔSOC be the change in battery charge corresponding to the i-th effective charging segment. j SOH represents the change in battery charge corresponding to the j-th effective charging segment. iThe battery health status corresponding to the i-th valid charging segment; SOH final This refers to the battery health status corresponding to the target vehicle battery.

[0042] In a specific implementation, to ensure the validity of the calculation, step S10 in this embodiment may include: Anomaly identification is performed on the battery charging logs of the target vehicle battery to determine the abnormal logs; Remove the abnormal logs from the battery charging logs to identify valid charging logs; The valid charging logs are filtered to obtain valid charging segments.

[0043] It should be noted that, in order to ensure the validity of the calculation, it is necessary to exclude obviously abnormal data in the battery charging log. Therefore, the battery charging log of the target vehicle battery can be anomaly identified first, abnormal logs can be removed, and then the valid charging log can be filtered to obtain valid charging segments.

[0044] In the specific implementation, anomaly detection is based on at least one of the following: State of charge continuity; The rationality of the current direction and magnitude; SOC monotonicity and non-decreasingness; Vehicle speed and gear selection are consistent; Matching of charging time with changes in SOC.

[0045] In the specific implementation, the process of identifying abnormal logs based on the continuity of charging status can be as follows: the charging status field must be continuously "parking and charging" within the segment. If a non-"parking and charging" status occurs in the middle that is greater than or equal to a certain threshold (such as 1 minute), it is considered an interruption and is marked as an abnormal log.

[0046] The specific process for identifying abnormal logs based on the reasonableness of current direction and magnitude can be as follows: The total current I < 0 (according to the convention: discharge is positive, charging is negative), and |I| ≥ Imin (e.g., 1A), excluding weak leakage or noise, and the current fluctuation should not be too large (e.g., the standard deviation exceeds 50% of the mean, which is considered unstable). Otherwise, it is marked as an abnormal log.

[0047] The specific process for identifying anomalous logs based on the monotonic non-decreasing property of SOC can be as follows: SOC sequences acquired at close times must be monotonically non-decreasing (equal values ​​are allowed, but decreasing values ​​are not). If a decrease in SOC occurs (e.g., due to a jump in BMS calibration), it is necessary to determine whether the correction is reasonable. If the voltage change is accompanied by a sudden change and the duration is short (<30s), it can be smoothed out. Otherwise, mark it as an exception in the log.

[0048] The specific process for ensuring consistency between vehicle speed and gear can be as follows: The vehicle speed should be approximately 0 (e.g., ≤ 2 km / h). The gear should be in neutral (N) or park (P); If the vehicle speed is >5 km / h or the gear is not N or P, it will be marked as an abnormal log.

[0049] The specific process for matching charging time with SOC change can be as follows: The SOC change in the log is compared with the collection time interval. The standard SOC change is calculated based on the collection time interval. If the SOC change in the log is greater than the standard SOC change and the excess is large (e.g., >50%), it is marked as an abnormal log.

[0050] This embodiment provides a method for determining battery health. Since battery health is monitored based on the number of consecutive sampling days and the number of sampling logs, it ensures that battery health monitoring can be achieved without collecting all data, and it can cover most vehicles. Thus, it ensures that the battery health of vehicles can be monitored in the cloud.

[0051] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S10, the battery health determination method further includes steps S01 to S03: Step S01: Obtain the number of consecutive sampling days and the number of sampling times.

[0052] It should be noted that the number of consecutive sampling days and the number of sampling times can be pre-calibrated by the administrator of the battery health determination device according to the preset coverage threshold set by actual needs.

[0053] Step S02: Collect the battery operation log of the target vehicle battery based on the number of consecutive sampling days and the number of sampling times.

[0054] Step S03: Filter the battery operation log to obtain the battery charging log.

[0055] In practical use, in some cases, the battery management system does not have high computing power. If the filtering of logs is put into the battery management system, it may cause the battery management system to run slowly, lag, or even malfunction. Based on this, the battery management system can directly collect logs from the target vehicle battery according to the number of consecutive sampling days and the number of collections to obtain the battery operation logs without further differentiation.

[0056] Understandably, logs collected in this way include not only battery charging logs, but also logs from non-charging conditions such as battery discharging and battery balancing. Therefore, it is necessary to filter the battery operation logs, removing parts unrelated to charging or extracting only the parts related to charging, in order to obtain the battery charging logs.

[0057] In practice, the filtering of battery operation logs can be determined based on specific fields in the logs. For example, if the collected battery operation logs contain a field called "charging status", then logs with the value "charging while parked" for that field can be filtered as battery charging logs.

[0058] Of course, in some cases, the data quality of the collected battery operation logs may be poor and may not contain fields that separately indicate whether charging has occurred. In such cases, fields such as vehicle speed, total current (<0), and gear position can be used for auxiliary filtering. For example, battery operation logs with a vehicle speed of 0 (or close to 0), a negative total current, and a parking gear can be used as battery charging logs.

[0059] In specific implementation, to ensure the rationality of the data collection as much as possible, step S01 in this embodiment may include: Obtain the vehicle application type corresponding to the target vehicle battery; The average daily mileage and average daily coverage of the target vehicle battery are determined based on the vehicle application type. The average daily mileage is the average daily mileage of the vehicle corresponding to the target vehicle battery, and the average daily coverage is the probability of collecting effective charging segments on a single day. The number of consecutive sampling days is determined based on the full-charge range of the target vehicle battery and the average daily mileage. The number of sampling times is determined based on the number of consecutive sampling days and the average daily coverage.

[0060] It should be noted that the average daily mileage refers to the average daily mileage of the vehicle corresponding to the target on-board battery, and the average daily coverage rate is the probability that a valid charging segment can be collected on a single day, that is, the probability that a corresponding valid charging segment can be obtained by collecting logs on a single day.

[0061] In practice, based on the frequency of vehicle use or registration information, vehicle application types can be divided into two categories: operational type and residential type. If the vehicle application type is operational, it means that the vehicle equipped with the target vehicle battery is a high-frequency vehicle such as a ride-hailing vehicle or a taxi. The charging methods of such vehicles are similar, so they can be regarded as a target group. Based on this, log data of such target group for one day can be extracted to determine the proportion of vehicles with effective charging segments that can be collected to the target group vehicles. This proportion is used as the daily average coverage rate. At the same time, the average mileage of the target vehicle in one day is calculated and used as the daily average mileage. If the vehicle application type is household, it means that the vehicle equipped with the target battery is a household vehicle. However, the charging methods of different household vehicles may vary greatly depending on the user, making it difficult to use the habits of a group as a reference. In this case, the usage data of the vehicle equipped with the target battery over a period of time (such as a week or a month) can be obtained. Based on this data, the total mileage and the number of charging times during that period can be calculated. The product of the total mileage and the number of days included in the period is the average daily mileage, and the product of the number of charging times and the number of days included in the period is the average daily coverage.

[0062] The number of charging times can be the number of valid charging segments included in the usage data collected within a certain period. For example, if the usage data of a vehicle equipped with the target onboard battery is collected within a certain period (such as a week or a month), and the usage data is analyzed to determine that there are 3 valid charging segments included, then the number of charging times within that period is 3.

[0063] When determining the average daily coverage, whether the collected data includes valid charging segments can be determined by manual annotation or by automatic identification by the battery health determination device based on the above-mentioned criteria for determining valid charging segments. This embodiment does not impose any restrictions on this.

[0064] In practical use, the full-charge mileage of the target vehicle battery can be divided by the average daily mileage. Then, the product is rounded up, and the rounded value is used as the number of consecutive sampling days. Then, the number of sampling days and the average daily coverage are combined to calculate the number of sampling times required to make the overall coverage greater than or equal to the preset coverage threshold.

[0065] In practical implementation, to ensure that the number of data collections is minimized, the step of determining the number of data collections based on the number of consecutive sampling days and the average daily coverage rate described in this embodiment may include: Set the number of tests to be performed to the minimum value of the number of tests; The sampling coverage rate is calculated based on the number of tests to be performed, the number of consecutive sampling days, and the average daily coverage rate. If the sampling coverage rate is greater than or equal to a preset coverage threshold, then the number of times to be detected is taken as the number of times to collect samples; or, If the sampling coverage rate is less than the preset coverage rate threshold, the number of tests to be performed is increased to generate a new number of tests to be performed, and the process of calculating the sampling coverage rate based on the number of tests to be performed, the number of consecutive sampling days, and the average daily coverage rate is returned.

[0066] It should be noted that, without affecting the overall coverage, in order to minimize the amount of data that needs to be processed in the cloud, the number of collections should be kept as small as possible under certain conditions. Therefore, the number of collections to be detected can be set to the minimum value first, and then the sampling coverage can be calculated based on the number of collections to be detected, the number of consecutive sampling days, and the average daily coverage using the coverage calculation formula. The formula for calculating coverage can be: Cov = 1 - (1 - β) M*N In the formula, Cov is the sampling coverage rate, β is the daily average coverage rate, M is the number of tests to be performed, and N is the number of consecutive sampling days.

[0067] In practical use, if the sampling coverage rate is greater than or equal to the preset coverage rate threshold, it means that the log sampling based on the number of tests to be performed and the number of consecutive sampling days can ensure that the overall coverage rate is greater than or equal to the preset coverage rate threshold. Therefore, the number of tests to be performed can be used as the number of collections. If the sampling coverage rate is less than the preset coverage rate threshold, it means that the overall coverage rate that can be achieved by sampling logs based on the number of tests to be performed and the number of consecutive sampling days is insufficient. Therefore, the number of tests to be performed can be increased to generate a new number of tests to be performed, and the step of calculating the sampling coverage rate based on the number of tests to be performed, the number of consecutive sampling days, and the average daily coverage rate can be returned to be executed.

[0068] In order to ensure that the number of collections is as small as possible, when increasing the number of tests, the step size can be 1, that is, the number of tests is increased by 1 each time.

[0069] In practical implementation, based on actual data analysis, in most cases, the product of the calculated number of tests to be performed and the number of consecutive sampling days will be less than the duration of a full monitoring cycle. In this case, data can be collected directly within a full monitoring cycle based on the number of tests to be performed and the number of consecutive sampling days to obtain the battery operation log of the target vehicle battery. For example, assuming the full monitoring cycle is 30 days, and the number of tests to be performed is M, and the number of consecutive sampling days is N, then in most cases, M*N≤30. In this case, M consecutive sampling days can be performed within 30 days, and the collected data can be used as the battery operation log of the target vehicle battery.

[0070] However, in some cases, such as when a private car is used very infrequently (e.g., when the user is on a business trip), the product of the number of tests to be performed and the number of consecutive sampling days may be greater than the duration of a full monitoring cycle, even if the sampling coverage rate is greater than or equal to the preset coverage rate threshold. This indicates that the vehicle is used too infrequently, and even if all data is collected in a full monitoring cycle, it is difficult to calculate the battery health of the vehicle's onboard battery. For such vehicles, they can be tagged, and the battery health of the vehicle's onboard battery can be calculated using data collected over two or more comprehensive monitoring cycles.

[0071] For example: Assuming the full monitoring cycle is 30 days, the number of tests to be performed is M, and the number of consecutive sampling days is N, if M*N>30, then we can further determine whether M*N is less than or equal to 30*2. If so, then in the two full monitoring cycles, we will perform M / 2 consecutive samplings for N days each time, and then summarize the data collected in the two full monitoring cycles into the battery operation log of the target vehicle battery to calculate the battery health of the vehicle battery. If M*N is greater than 30*2, it can be further determined whether it is less than or equal to 30*3. If so, the data collected in three comprehensive monitoring cycles is summarized into the battery operation log of the target vehicle battery to calculate the battery health of the vehicle battery, and so on.

[0072] Understandably, in this situation, even if all data is collected in one comprehensive monitoring cycle, it is difficult to calculate the battery health of the vehicle's on-board battery. If full data collection is used to calculate the battery health, data collected in full for at least two comprehensive monitoring cycles is required to calculate the battery health. However, if the above method is used for data collection, the amount of data collected can still be smaller than that collected for full data collection. For example, assuming the full monitoring cycle is 30 days, the number of tests to be determined is M, and the number of consecutive sampling days is N, if M*N>30 and M*N≤30*2, then in the two full monitoring cycles, M / 2 consecutive samplings for N days will be performed respectively. The amount of data to be collected is (M / 2)*N*2=M*N days of data. However, if full data collection is performed to calculate battery health, then 30*2 days of data need to be collected. At this time, the amount of data used to calculate battery health using the battery health determination method of this embodiment is still less than the amount of data collected for full data collection.

[0073] This embodiment provides a method for determining battery health. By analyzing and setting the corresponding number of consecutive sampling days and sampling times based on actual conditions, this embodiment ensures the rationality of the settings.

[0074] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S10 includes steps S101 to S104: Step S101: Divide the battery charging log of the target vehicle battery and generate at least one charging segment.

[0075] Step S102: Obtain the battery type of the target vehicle battery.

[0076] Step S103: If the battery type is the first type, then determine the edge abrupt change segment according to the battery charging curve corresponding to the target vehicle battery.

[0077] Step S104: Based on the edge abrupt change segment, filter the charging segments of the target vehicle battery's battery charging log to obtain valid charging segments.

[0078] It should be noted that the battery charging logs of the target vehicle battery can be segmented. Battery charging logs with similar collection times and corresponding battery levels that are similar and increasing in a certain direction can be grouped into the same segment, thus obtaining at least one charging segment. For example, battery charging logs with similar collection times, where the corresponding battery levels are consistent or increasing as the collection time progresses, can be grouped into the same segment.

[0079] In practical applications, based on the differences in charging characteristics, batteries can be divided into two types: Type I and Type II. Type I batteries are those with non-smooth charging curves, such as phosphorus-iron batteries; Type II batteries are those with smooth charging curves, such as ternary lithium batteries.

[0080] The selection methods for these two types of batteries will differ. If the battery type is the first type, the battery charging curve corresponding to the target vehicle battery can be obtained first. Based on the battery charging curve, the edge abrupt change segment can be determined. Then, based on the edge abrupt change segment, the battery charging log of the target vehicle battery can be filtered to obtain the effective charging segment.

[0081] In practice, the corresponding charge values ​​for abrupt changes vary slightly across different battery manufacturers and types. You can first refer to the battery charging curve (OCV-SOC) factory data for each manufacturer (or compile actual sample data from a batch of similar vehicles to compare SOC with the highest voltage of a single cell), find the SOCs x and y corresponding to the boundaries of the two abrupt charging segments, and then filter the SOCs within the charging segments. st ≥ x and SOC end The portion of the charging range ≤y is considered a valid charging segment.

[0082] The specific process for identifying the boundary of abrupt charging segments can be as follows: Obtain the highest single-cell voltage V of a batch of battery vehicles of the same type during steady-state charging or resting, sort all V values, and obtain the following based on the sorting: Q1 = 25th percentile in the sorting Q3 = 75th percentile in the sorting IQR=Q3 Q1 Next, calculate Threshold. low = Q1 – 1.5 × IQR; Threshold high = Q1 + 1.5 × IQR; Use the OCV-SOC mapping relationship to determine the Threshold. low The corresponding SOC value is used as x to determine the Threshold. high The corresponding SOC value is used as y.

[0083] In a specific implementation, in order to effectively filter effective charging segments for the second type of battery, the following step after S102 in this embodiment may also include: If the battery type is the second type, then the estimation error corresponding to each charging segment is obtained. The second type of battery is a battery with a smooth charging curve. Charging segments with an estimated error less than or equal to a preset error threshold are considered valid charging segments.

[0084] It should be noted that the charging curve of the second type of battery is close to linear growth with virtually no drop-off curve. The battery health can be calculated using the entire range of charging segments. However, if the change in charge is too small, it may lead to excessive calculation errors, resulting in poor battery health. Therefore, it is necessary to exclude such charging segments that may cause excessive errors. Based on this, if the battery type is the second type, the estimated error corresponding to each charging segment can be obtained. Then, the charging segments with estimated errors less than or equal to a preset error threshold are considered as valid charging segments.

[0085] The estimation error for the charging segment can be calculated as follows: er = (1 / △SOC) * 100% In the formula, er represents the estimation error corresponding to the charging segment, and △SOC represents the change in battery capacity corresponding to the charging segment.

[0086] In one possible implementation of this embodiment, when the frequency of vehicle charging is limited and the charging increment is small (limited ΔSOC), the IQR method can be used to distinguish fine-grained "flat" voltage differences (i.e., the threshold of the highest voltage of a single cell) within the ΔSOC segment. low Q1, Q2, Q3, Threshold high Let any two corresponding electricity values ​​be x and y, such that soc_st ≥ x and soc_end ≤ y. Of course, while increasing the coverage, this will also amplify the estimation error.

[0087] This embodiment provides a method for determining battery health. Based on different battery types, this embodiment uses different methods to screen effective charging segments, ensuring the effectiveness of subsequent battery health calculations. It can meet the monitoring requirements of relevant departments, car manufacturers, and power battery manufacturers for battery health, and can be used for monitoring different ternary and lithium iron phosphate batteries. It is also not limited by battery production batches or latitude and longitude.

[0088] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the battery health determination method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0089] This application also provides a battery health determination device, please refer to... Figure 4 The battery health determination device includes: The filtering module 10 is used to filter the charging segments of the battery charging log of the target vehicle battery to obtain valid charging segments. The battery charging log is collected based on the number of consecutive sampling days and the number of collections. The number of consecutive sampling days is the number of days of the continuous sampling log, and the number of collections is the number of times continuous sampling is performed. The calculation module 20 is used to calculate the battery health based on the effective charging segments to obtain the battery health corresponding to each effective charging segment. The weighting module 30 is used to weight the battery health of each valid charging segment to determine the battery health of the target vehicle battery.

[0090] The battery health determination device provided in this application, employing the battery health determination method described in the above embodiments, can solve the technical problem that automakers find it difficult to monitor the health of on-board power batteries in their vehicles via the cloud. Compared with the prior art, the beneficial effects of the battery health determination device provided in this application are the same as those of the battery health determination method provided in the above embodiments, and other technical features of the battery health determination device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0091] This application provides a battery health determination device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the battery health determination method in the first embodiment described above.

[0092] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the battery health determination device in the embodiments of this application. The battery health determination device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The battery health determination device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0093] like Figure 5 As shown, the battery health determination device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the battery health determination device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the battery health determination device to communicate wirelessly or wiredly with other devices to exchange data. While various systems are shown in the figure, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0094] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0095] The battery health determination device provided in this application, employing the battery health determination method described in the above embodiments, can solve the technical problem that automakers find it difficult to monitor the health of on-board power batteries in their vehicles via the cloud. Compared with the prior art, the beneficial effects of the battery health determination device provided in this application are the same as those of the battery health determination method provided in the above embodiments, and other technical features of this battery health determination device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0096] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0097] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0098] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the battery health determination method in the above embodiments.

[0099] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0100] The aforementioned computer-readable storage medium may be included in the battery health determination device; or it may exist independently and not assembled into the battery health determination device.

[0101] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the battery health determination device, the battery health determination device causes the following: it filters charging segments from the battery charging logs of the target vehicle battery to obtain valid charging segments. The battery charging logs are collected based on the number of consecutive sampling days and the number of collections, where the number of consecutive sampling days is the number of days in the continuous sampling logs and the number of collections is the number of times continuous sampling is performed; it calculates the battery health based on the valid charging segments to obtain the battery health corresponding to each valid charging segment; and it weights the battery health corresponding to each valid charging segment to determine the battery health corresponding to the target vehicle battery.

[0102] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Python, Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0104] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0105] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described battery health determination method. This solves the technical problem that automakers find it difficult to monitor the health of on-board power batteries in their vehicles via the cloud. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the battery health determination method provided in the above embodiments, and will not be repeated here.

[0106] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the battery health determination method described above.

[0107] The computer program product provided in this application can solve the technical problem that automakers find it difficult to monitor the health of on-board power batteries in their vehicles via the cloud. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the battery health determination method provided in the above embodiments, and will not be repeated here.

[0108] All user-related data involved in this application (such as user privacy data, user behavior data, etc.) were obtained with the user's permission or consent; that is to say, when this application is used in a specific product or technology, user permission is required to obtain and process the relevant data, and the processing of the relevant data must comply with the relevant laws, regulations and regulatory standards of the relevant countries and regions.

[0109] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A method for determining battery health, characterized in that, The battery health determination method includes: The battery charging logs of the target vehicle battery are filtered to obtain valid charging segments. The battery charging logs are collected based on the number of consecutive sampling days and the number of collections. The number of consecutive sampling days is the number of days in the continuous sampling logs, and the number of collections is the number of times continuous sampling is performed. The health status of the battery is calculated based on the effective charging segments to obtain the battery health status corresponding to each effective charging segment. The battery health of the target vehicle battery is determined by weighting the battery health of each effective charging segment.

2. The battery health determination method as described in claim 1, characterized in that, Before filtering the charging segments of the target vehicle battery's charging log to obtain valid charging segments, the process also includes: Obtain the number of consecutive sampling days and the number of sampling times; The battery operation log of the target vehicle battery is collected based on the number of consecutive sampling days and the number of sampling times; The battery operation log is filtered to obtain the battery charging log.

3. The battery health determination method as described in claim 2, characterized in that, The acquisition of the number of consecutive sampling days and the number of sampling times includes: Obtain the vehicle application type corresponding to the target vehicle battery; The average daily mileage and average daily coverage of the target vehicle battery are determined based on the vehicle application type. The average daily mileage is the average daily mileage of the vehicle corresponding to the target vehicle battery, and the average daily coverage is the probability of collecting effective charging segments on a single day. The number of consecutive sampling days is determined based on the full-charge range of the target vehicle battery and the average daily mileage. The number of sampling times is determined based on the number of consecutive sampling days and the average daily coverage.

4. The battery health determination method as described in claim 3, characterized in that, The step of determining the number of sampling times based on the number of consecutive sampling days and the average daily coverage includes: Set the number of tests to be performed to the minimum value of the number of tests; The sampling coverage rate is calculated based on the number of tests to be performed, the number of consecutive sampling days, and the average daily coverage rate. If the sampling coverage rate is greater than or equal to a preset coverage threshold, then the number of times to be detected is taken as the number of times to collect samples; or, If the sampling coverage rate is less than the preset coverage rate threshold, the number of tests to be performed is increased to generate a new number of tests to be performed, and the process of calculating the sampling coverage rate based on the number of tests to be performed, the number of consecutive sampling days, and the average daily coverage rate is returned.

5. The battery health determination method as described in claim 1, characterized in that, The step of filtering charging segments from the battery charging logs of the target vehicle battery to obtain valid charging segments includes: The battery charging log of the target vehicle battery is divided into segments to generate at least one charging segment; Obtain the battery type of the target vehicle battery; If the battery type is the first type, then the abrupt change segment at the edge is determined according to the battery charging curve corresponding to the target vehicle battery. The first type of battery is a battery with a non-smooth charging curve. Based on the edge-change segment, the battery charging log of the target vehicle battery is filtered to obtain valid charging segments.

6. The battery health determination method as described in claim 5, characterized in that, After obtaining the battery type of the target vehicle battery, the process further includes: If the battery type is the second type, then the estimation error corresponding to each charging segment is obtained. The second type of battery is a battery with a smooth charging curve. Charging segments with an estimated error less than or equal to a preset error threshold are considered valid charging segments.

7. The battery health determination method according to any one of claims 1-6, characterized in that, The step of filtering charging segments from the battery charging logs of the target vehicle battery to obtain valid charging segments includes: Anomaly identification is performed on the battery charging logs of the target vehicle battery to determine the abnormal logs; Remove the abnormal logs from the battery charging logs to identify valid charging logs; The valid charging logs are filtered to obtain valid charging segments; Anomaly detection is based on at least one of the following: State of charge continuity; The rationality of the current direction and magnitude; SOC monotonicity and non-decreasingness; Vehicle speed and gear selection are consistent; Matching of charging time with changes in SOC.

8. A battery health determination device, characterized in that, The battery health determination device includes: The filtering module is used to filter the charging segments of the battery charging log of the target vehicle battery to obtain valid charging segments. The battery charging log is collected based on the number of consecutive sampling days and the number of collections. The number of consecutive sampling days is the number of days of the continuous sampling log, and the number of collections is the number of times continuous sampling is performed. The calculation module is used to calculate the battery health based on the effective charging segments and obtain the battery health corresponding to each effective charging segment. The weighting module is used to weight the battery health of each valid charging segment to determine the battery health of the target vehicle battery.

9. A battery health determination device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the battery health determination method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the battery health determination method as described in any one of claims 1 to 7.

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