Battery capacity correction method suitable for intermittent data loss scene

Abstract

The invention provides a battery capacity correction method suitable for an intermittent data loss scene, and relates to the application field of a battery technology. The method comprises the following steps: in a mode based on intermittent data loss, marking a capacity actually corresponding to a unit SOC change in a discharge process as an actual denominator, clustering the actual denominator, obtaining a theoretical capacity corresponding to the unit SOC change, and marking the theoretical capacity as a standard denominator; according to the method, a difference value between a standard denominator and an actual denominator is utilized to carry out capacity compensation on a current data loss position, and only one of data loss positions of the same SOC is selected for compensation, so that a corrected ampere-hour integral capacity value is obtained. According to the method, the influence of data loss on the ampere-hour integral capacity value is greatly overcome, the universality and accuracy of the battery diagnosis technology in a data loss scene are improved, the feasibility of data loss vehicle diagnosis early warning is guaranteed, and a guarantee is provided for the safety of a new energy vehicle.

Description

Technical Field

[0001] The present invention relates to the field of battery application technology, and more specifically to a battery capacity correction method suitable for intermittent data loss scenarios. Background Art

[0002] Compared with traditional fuel vehicles, new energy vehicles have been popularized for a short time and their technology maturity is relatively low. In particular, the battery system often fails, resulting in vehicle fault alarms, breakdowns, and even fires. However, new energy vehicles have a large amount of data related to the battery system collected by vehicle manufacturers in the cloud, which makes it possible to diagnose the battery system. Companies can form early warnings of battery safety hazards or health status through the analysis of cloud big data, implement active maintenance based on the early warning results, and carry out predictive maintenance, thereby eliminating safety hazards in advance before failures occur.

[0003] However, due to the reliability problems or failures of the on-board data transmission device, poor mobile network signals, etc., some data is often lost, making the background storage data incomplete, especially having a serious impact on the battery capacity ampere-hour integral, which is a key parameter for battery diagnosis and will directly affect the battery diagnosis results. There are two data loss modes. One is intermittent data loss, that is, the data loss period is short from a few seconds to a few minutes but the frequency is high, resulting in a total loss of data of more than 70% in one vehicle operation; the other is large-scale data loss, that is, the data loss lasts for a long period of time, such as tens of minutes to several hours, so that the vehicle operation data cannot be used for battery diagnosis. Data loss occurs from time to time, and the intermittent data loss mode accounts for a large proportion, which has a serious impact on the background battery diagnosis results of some vehicles or even makes it impossible to diagnose. In this case, the relevant vehicles will be in a state without background supervision, which is not conducive to the investigation of battery hidden dangers.

[0004] Commonly used compensation methods for dealing with data loss include: sliding window average method, nearest filling method, interpolation fitting method, etc. However, for scenarios such as new energy vehicles, because the current is affected by the extremely high frequency of vehicle driving power changes during the discharge process, the current change also presents the characteristics of pulse-like violent fluctuations, that is, it changes rapidly. Therefore, in this special scenario, the above commonly used data compensation methods cannot accurately capture the true value of the current during the data loss period, and the compensation results cannot meet the application requirements of battery diagnosis. Summary of the invention

[0005] The present invention provides a battery capacity correction method suitable for intermittent data loss scenarios, aiming to solve the shortcomings of existing data compensation methods that cannot accurately capture the true value of current during the data loss period, and the compensation results cannot meet the application requirements of battery diagnosis.

[0006] The present invention adopts the following technical solution: A battery capacity correction method suitable for intermittent data loss scenarios includes the following steps: S1: Integrate ampere-hours based on current and time to obtain the capacity value corresponding to each time point; S2: record the capacity corresponding to each SOC change as the actual denominator; S3: Cluster the actual denominators to obtain the theoretical capacity corresponding to the unit SOC change, which is marked as the standard denominator; S4: Identify the location of lost data; S5: Use the difference between the standard denominator and the actual denominator to perform capacity compensation on the location where data is lost. For the same SOC location where data is lost, only one location is selected to compensate, thereby obtaining the corrected ampere-hour integrated capacity value.

[0007] In a preferred embodiment, the capacity is obtained by the following calculation formula in step S1: ; Where I is the current, Δt is the sampling time interval, i is the sampling number, Q original It is the capacity obtained by integrating ampere-hours.

[0008] In the above step S1, the initial value of the capacity is set to the rated capacity or 0.

[0009] In a preferred embodiment, the specific process of the above step S2 includes: (1) Record the position index = (j, ... m, n) where each SOC changes, where index is a 1*N array whose elements are the position numbers where the SOC changes, and N is the total number of times the SOC changes; (2) Calculate the actual denominator of the SOC change and obtain the actual denominator array: VD=(Δq1,Δq2,... Δq k ,...Δq N ), where Δq k =Q original (index(k))-Q original (index(k-1)), Q original is the capacity obtained by integrating ampere-hours; k is the position index in the index array.

[0010] Furthermore, the battery capacity correction method of the present invention further includes step S6: repeating steps S4 and S5 until the inspection of the lost data position is completed and the capacity correction is completed to obtain a corrected capacity array.

[0011] In a preferred implementation scheme, the clustering in step S3 is specifically to select a class with a larger average value as the target class, and to clean the target class of outliers. After removing the outliers, the average value of the remaining data points is obtained; or the value with the largest density is obtained according to the probability density distribution, or the median, mode, maximum value and other methods are used to obtain the standard denominator.

[0012] In a preferred embodiment, the clustering algorithm adopts Kmean or Dbscan.

[0013] The battery capacity correction method of the present invention is applied to a new energy vehicle battery system or an energy storage battery system.

[0014] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following advantages: The battery capacity correction method of the present invention is based on the SOC calculation principle of the battery management system, and utilizes the principle that the rate of battery SOC decrease is usually a constant. The standard denominator is obtained through a clustering algorithm, and the capacity is compensated according to the difference between the actual capacity corresponding to the SOC change and the standard denominator, so as to realize the correction of capacity data, improve the versatility and accuracy of battery diagnosis technology in data loss scenarios, and greatly overcome the influence of data loss on the ampere-hour integrated capacity value, so that accurate and safe estimation of battery health status can still be achieved in data loss scenarios, thereby providing protection for the safety of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of a flow chart of an embodiment of the present invention.

[0016] Figure 2 A schematic diagram of a location of lost data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Refer to the following Figure 1 The specific implementation of the present invention is described. In order to fully understand the present invention, many details are described below, but for those skilled in the art, the present invention can be implemented without these details. For well-known components, methods and processes, they are not described in detail below.

[0018] This embodiment provides a battery capacity correction method applicable to intermittent data loss scenarios, including the following steps: Step 1: Integrate the ampere-hour based on the current and time to obtain the capacity value corresponding to each time point. The capacity is obtained using the following formula: ; Where I is the current, Δt is the sampling time interval, i is the sampling number, Q original It is the capacity obtained by integrating ampere-hours.

[0019] The initial value of the above capacity is preferably set to the rated capacity, and may also be set to 0 or other fixed values.

[0020] Step 2: Record the capacity corresponding to each SOC change as the actual denominator. The details are as follows: (1) Record the position where each SOC changes, index = (j, ... m, n), where index is a 1*N array whose elements are the position numbers where the SOC changes, and N is the total number of times the SOC changes.

[0021] (2) Calculate the actual denominator of the SOC change and obtain the actual denominator array: VD=(Δq1,Δq2,...Δq k ,...Δq N ) Among them, Δq k =Q original (index(k))-Q original (index(k-1)), k is the position index in the index array.

[0022] Step 3: Cluster the actual denominators to obtain the theoretical capacity corresponding to the unit SOC change, which is marked as the standard denominator VDI.

[0023] Clustering can use Kmean or Dbscan clustering algorithm.

[0024] The clustering in this embodiment specifically selects a class with a larger average value as a target class, performs outlier cleaning on the target class, and after removing the outliers, obtains the average value of the remaining data points.

[0025] In other embodiments, clustering may also be performed by obtaining the value with the largest density after probability density distribution, or obtaining the median, mode, maximum value, etc. to obtain a standard denominator.

[0026] Step 4: Identify the location where the data is lost. Specifically, identify the capacity value corresponding to each time point. If there is no corresponding capacity value at that time point, it is the location where the data is lost. Figure 2 As shown, Figure 2 The red dotted box in the middle marks the location where data loss occurs.

[0027] Step 5. Use the difference between the standard denominator and the actual denominator, that is, the compensation amount = VDI-VD(k), where VD(k) is the actual SOC denominator corresponding to the position where the data is lost, to perform capacity compensation on the position where the data is lost, mark the SOC value corresponding to the compensated position, and thus obtain the corrected ampere-hour integrated capacity value.

[0028] When the capacity compensation is performed on the data loss position, before the SOC changes, other data loss positions of the SOC are no longer compensated.

[0029] Step 6: Repeat steps 4 and 5 until the inspection of the lost data position is completed and the capacity correction is completed to obtain the corrected capacity array.

[0030] The battery capacity correction method of the present invention can be applied to a new energy vehicle battery system or an energy storage battery system.

[0031] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A battery capacity correction method suitable for intermittent data loss scenarios, characterized in that: The following steps are involved: S1: Integrate ampere-hours based on current and time to obtain the capacity value corresponding to each time point; S2: record the capacity corresponding to each SOC change as the actual denominator; S3: Cluster the actual denominators to obtain the theoretical capacity corresponding to the unit SOC change, which is marked as the standard denominator; S4: Identify the location of lost data; S5: Use the difference between the standard denominator and the actual denominator to perform capacity compensation on the location where data is lost. For the same SOC location where data is lost, only one location is selected to compensate, thereby obtaining the corrected ampere-hour integrated capacity value.

2. The algorithm for solving the SOC denominator array in the data loss mode according to claim 1, characterized in that: The step S1 uses the following calculation formula to obtain the capacity: ; Where I is the current, Δt is the sampling time interval, i is the sampling number, Q original It is the capacity obtained by integrating ampere-hours.

3. The algorithm for solving the SOC denominator array in the data loss mode according to claim 1 or 2, characterized in that: In step S1, the initial value of the capacity is set to the rated capacity or 0.

4. The algorithm for solving the SOC denominator array in the data loss mode according to claim 2, characterized in that: The specific process of step S2 includes: (1) Record the position index = (j, ... m, n) where each SOC changes, where index is a 1*N array whose elements are the position numbers where the SOC changes, and N is the total number of times the SOC changes; (2) Calculate the actual denominator of the SOC change and obtain the actual denominator array: VD=(Δq1,Δq2,... Δq k ,...Δq N ), where Δq k =Q original (index(k))-Q original (index(k-1)), Q original is the capacity obtained by integrating ampere-hours; k is the position index in the index array.

5. A battery capacity correction method suitable for intermittent data loss scenarios as claimed in claim 4, characterized in that: The method further includes step S6: repeating steps S4 and S5 until the inspection of the lost data position is completed and the capacity correction is completed to obtain a corrected capacity array.

6. A battery capacity correction method suitable for intermittent data loss scenarios as claimed in claim 1, characterized in that: The clustering in step S3 is specifically to select a class with a larger average value as the target class, and clean the target class for outliers. After removing the outliers, the average value of the remaining data points is obtained; or the value with the largest density is obtained according to the probability density distribution, or the median, mode, and maximum value methods are used to obtain the standard denominator.

7. A battery capacity correction method suitable for intermittent data loss scenarios according to claim 1 or 6, characterized in that: The clustering algorithm adopts Kmean or Dbscan.

8. The battery capacity correction method for intermittent data loss scenario according to claim 1, characterized in that: The correction method is applied to new energy vehicle battery systems or energy storage battery systems.

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