Battery pack capacity estimation method under abnormal charging interference of electric vehicle

Abstract

The application discloses a battery pack capacity estimation method under abnormal charging interference of an electric vehicle. The method comprises a parameter setting module, a charging data selection module, an abnormal charging data processing module, an abnormal charging data processing module and an optimal capacity estimation module. The parameter setting module is used for setting necessary parameters, the charging data selection module refers to selecting signals for capacity estimation, the abnormal charging data processing module is used for finding out abnormalities in the charging data, the abnormal charging data processing module is used for eliminating the interference of abnormal data, and the optimal capacity estimation module is used for giving a reasonable capacity value. The application realizes the estimation of the battery pack capacity of the electric vehicle under abnormal charging through special processing of abnormal charging data, avoids the interference of abnormal charging on the estimation result, improves the accuracy of the estimation of the battery pack capacity of the electric vehicle, and has a wide market application prospect.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, and in particular to a method for estimating battery pack capacity under abnormal charging interference in electric vehicles. Background Technology

[0002] According to statistics, the transportation industry is the world's second-largest source of greenhouse gas emissions. Electric vehicles emit no exhaust fumes during operation, which is highly beneficial to improving environmental quality. Due to their environmental friendliness and ease of driving, and with increasing public awareness of environmental protection, electric vehicles are gaining popularity and their market share is constantly rising. As the core component of electric vehicles, the battery not only affects the price but is also a decisive factor in their safety and driving range.

[0003] Battery capacity is a crucial indicator of battery performance, directly determining a vehicle's driving range and operating time. Battery capacity degradation is an unavoidable issue during use. From a safety perspective, it's generally necessary for the battery's remaining capacity to be no less than 80% of its initial capacity. Therefore, accurately estimating the capacity of electric vehicle batteries is a vital technical challenge.

[0004] Currently, commonly used battery capacity estimation methods include direct measurement, calculation methods based on state of charge (SOC), and data-driven methods. Data-driven methods are a relatively new development, primarily based on IoT and AI technologies, especially deep learning. They learn patterns and rules directly from data and then classify or predict new data. However, these methods still require accurate capacity values ​​as sample labels. Coulomb counting, with its simplicity and practicality, is widely used in practice.

[0005] Electric vehicles encounter many abnormal situations during charging, such as data acquisition anomalies and intermittent charging. Abnormal charging data can severely affect battery capacity estimation, leading to significant deviations in battery capacity estimates using current technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies in this field, this invention provides a method for estimating the battery pack capacity of electric vehicles under abnormal charging interference, thereby achieving accurate estimation of the battery pack capacity and improving the accuracy of battery pack capacity estimation.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for estimating battery pack capacity under abnormal charging interference in electric vehicles includes:

[0009] The system comprises a parameter setting module, a charging data selection module, a charging data anomaly detection module, an anomaly charging data processing module, and an optimal capacity estimation module. The parameter setting module sets the necessary parameters for calculating the battery pack capacity, including a charging data length threshold, an interruption threshold, and a reference capacity. The charging data selection module selects current, voltage, time, and SOC signals from a large number of measured battery pack signals for capacity estimation. The charging data anomaly detection module analyzes the selected charging data to identify anomalies, including data length anomalies, sampling anomalies, current anomalies, voltage anomalies, and SOC anomalies. The anomaly charging data processing module processes the anomaly data to eliminate interference. The optimal capacity estimation module calculates a reasonable battery pack capacity value.

[0010] Beneficial effects

[0011] The method of this invention, through special processing of charging anomaly data, can estimate the battery pack capacity of electric vehicles under charging anomaly conditions, avoiding interference from charging anomalies on the estimation results. Compared with existing methods, the method of this invention provides reasonable estimation results and has broad market application prospects. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating a method for estimating battery pack capacity under abnormal charging interference in an electric vehicle, as provided in an embodiment of the present invention.

[0013] Figure 2 This is a schematic diagram of a battery pack capacity estimation method for electric vehicles under abnormal charging interference provided in an embodiment of the present invention;

[0014] Figure 3 An example of an abnormal charging of an electric vehicle is provided as an embodiment of the present invention;

[0015] Figure 4 This is another example of an abnormal charging situation for an electric vehicle provided in an embodiment of the present invention;

[0016] Figure 5 This is yet another example of an abnormal charging of an electric vehicle provided in the embodiments of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the following description is for the purpose of better understanding the process of the present invention, and not for limiting the present invention.

[0018] This invention provides a battery pack capacity estimation method for electric vehicles under abnormal charging interference, comprising a parameter setting module, a charging data selection module, a charging data anomaly detection module, an abnormal charging data processing module, and an optimal capacity estimation module, such as... Figure 1 As shown.

[0019] The parameter setting module is used to set the necessary parameters for calculating the battery pack capacity. These parameters mainly include a charging data length threshold, an interruption threshold, and a reference capacity. The charging data length threshold is used to determine whether the charging data length is sufficient for capacity calculation. The charging data length threshold needs to be determined according to the specific sampling process, and it is advisable to set the charging data length threshold to allow for 10 minutes of charging. The interruption threshold is used to determine whether there is an interruption in the charging data. The interruption threshold needs to be determined according to the specific sampling process, and the optimal value of the interruption threshold is set to 10 to 20 times the normal sampling interval. The reference capacity is used to find the best capacity estimate from multiple calculated capacity values. The initial value of the reference capacity is determined according to the initial capacity of the battery pack, and the reference capacity needs to be updated as the electric vehicle operates.

[0020] The charging data selection module selects current, voltage, time, and SOC signals from a large number of measured battery pack signals for battery pack capacity estimation; the basic calculation formula for battery pack capacity estimation is as follows:

[0021]

[0022] In the formula, i(·) is the charging current, soc is the state of charge, time is in seconds, and η is the coulombic efficiency, which is taken as η = 1 for lithium batteries.

[0023] The charging data anomaly detection module is used to analyze the selected charging data and find out the anomalies. The anomalies include data length anomalies, sampling anomalies, current anomalies, voltage anomalies, and SOC anomalies. The anomaly detection is to detect whether there are short data lengths, duplicate data, sampling interruptions, charging interruptions (including intermittent charging), and abnormal SOC growth.

[0024] The abnormal charging data processing module is used to process abnormal data and eliminate its interference. For short data lengths, it uses a reference capacity as the optimal capacity estimate. For duplicate data, it removes duplicate data. For sampling and charging interruptions, if the interruption time exceeds a set threshold, it uses a segmented calculation method to find the optimal capacity estimate from the segmented calculated capacity based on the reference capacity. For intermittent charging, it identifies and removes data points indicating charging interruptions. For abnormal SOC growth, it uses interpolation to estimate a reasonable SOC value.

[0025] The segmented calculation method involves dividing a charging process into several segments based on a set interruption threshold. If the length of a sub-segment is not less than a set length threshold, the capacity value of the corresponding segment is calculated. After calculating the capacity values ​​of all segments, the capacity value closest to the reference capacity is found as the estimated capacity of the charging process.

[0026] The aforementioned abnormal SOC growth is addressed by using interpolation to estimate a reasonable SOC value. This involves performing interpolation calculations on the SOC based on changes in charging voltage to estimate the SOC change over charging time.

[0027] The optimal capacity estimation module is used to estimate a reasonable battery capacity value. For charging data without abnormalities, the battery pack capacity is directly calculated using formula (1) and the reference capacity is updated. For charging data with abnormalities, the battery pack capacity is calculated using the following formula in conjunction with the processing of abnormal data.

[0028]

[0029] In the formula, γ is a charging indicator factor. If charging is normal, let γ = 1; if charging is interrupted, let γ = 0. The reference capacity is calculated based on the capacity obtained under conditions of no charging anomalies, and its update expression is:

[0030]

[0031] in, For the updated reference capacity, C r C is the reference capacity before the update. max The capacity is calculated for a charging process without charging anomalies, with coefficient α∈[0,1].

[0032] The charging indicator factor γ is used to indicate whether the charging data has actually charged the battery. The charging indicator factor is set based on whether the State of Charge (SOC) is increasing normally. If the change in SOC is negative, the corresponding charging indicator factor γ = 0; otherwise, γ = 1. Simultaneously, if the following formula is satisfied...

[0033]

[0034] In the formula, ε is a small positive number, ΔT is the sampling interval, then let γ = 1 for the corresponding data, otherwise let γ = 0; the value of the coefficient ε needs to be determined according to the specific charging process.

[0035] Example

[0036] This embodiment uses the charging process of an electric vehicle to estimate the battery pack capacity. A schematic diagram of the method of this invention is shown below. Figure 2 As shown in the figure. The specific implementation process of the present invention will be described in detail below.

[0037] First, set the necessary parameters for calculating the battery pack capacity. These parameters mainly include the charging data length threshold, the interruption threshold, and the reference capacity. The charging data length threshold is used to determine whether the charging data length is sufficient for capacity calculation; the interruption threshold is used to determine whether there is an interruption in the charging data; and the reference capacity is used to find the best capacity estimate from multiple calculated capacity values.

[0038] Then, from the numerous battery pack signals measured, current signals, voltage signals, time signals, and SOC signals used for battery pack capacity estimation are selected, and individual charging process data are separated.

[0039] Anomaly detection is performed on a complete charging process data to find data length anomalies, sampling anomalies, current anomalies, voltage anomalies and SOC anomalies; if no anomalies are found, the capacity is directly calculated using formula (1).

[0040] Next, abnormal charging data is processed to overcome the interference of abnormal data. If the data length is less than the length threshold, the reference capacity is used as the best capacity estimate. If there is a duplicate data problem, the duplicate data is removed. If there is a sampling interruption and a charging interruption, that is, the interruption time exceeds the set threshold, the data is segmented and the charging indicator factor γ of the segment with a length not less than the length threshold is calculated. If the battery capacity is found to be decreasing, the charging indicator factor γ corresponding to this data is set to 0, otherwise γ is set to 1. The battery pack capacity of the segment with a length not less than the length threshold is calculated using formula (2), and then the best capacity estimate is found from the segmented capacity calculated based on the reference capacity. If the SOC growth is abnormal, a reasonable SOC value is estimated using interpolation. If the charging is intermittent, the data of the charging interruption is found and removed, that is, the charging indicator factor γ corresponding to the data point is set to 0, the charging indicator factor is calculated, and then the battery pack capacity is calculated using formula (2).

[0041] Based on this, capacity calculation is performed to estimate a reasonable battery capacity value. For charging data without abnormalities, the battery pack capacity is calculated directly using formula (1) and the reference capacity is updated. For charging data with abnormalities, the battery pack capacity is calculated using formula (2) in conjunction with the processing of abnormal data.

[0042] With the charging data length threshold set to 70, the sampling interruption threshold set to 100s, the initial reference capacity set to 135, α = 0.7, and ε = 0.2, the comparative results of a group of electric vehicle battery pack capacity estimates are as follows: Figures 3-5 As shown. The parameters mentioned above need to be determined based on the specific charging process; the examples provided here are for reference only.

[0043] Figure 3In the example shown, there was a sampling interruption of more than 2000 seconds during the charging process. The battery pack capacity value estimated by the method of the present invention was 128.0, while the battery pack capacity value calculated directly using formula (1) was 232.4. Obviously, the calculation result of the method of the present invention is more reasonable.

[0044] Figure 4 In the example shown, the charging process was abnormal in SOC and intermittent charging. The SOC hardly changed in the first half and the charging current fluctuated greatly. The battery pack capacity value estimated by the method of the present invention was 115.2, while the battery pack capacity value calculated by directly using formula (1) was 446.2. The difference between the two is huge. The method of the present invention is closer to the reference capacity.

[0045] Figure 5 In the example shown, the charging process was intermittent, and the sampling was interrupted multiple times. The battery pack capacity estimated using the method of this invention was 133.9, while the battery pack capacity calculated directly using formula (1) was 182.6. The calculation result of the method of this invention is closer to the reference capacity. Figures 3-5 By comparing the calculation results, it can be seen that the estimated result of the method of the present invention is closer to the reference capacity, and the estimated capacity is more reasonable.

[0046] While the implementation steps of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of the invention. Any modifications made based on the present invention are within the protection scope of the present invention.

Claims

1. A method for estimating battery pack capacity under abnormal charging interference in electric vehicles, characterized in that, include: The system includes a parameter setting module, a charging data selection module, a charging data anomaly detection module, an abnormal charging data processing module, and an optimal capacity estimation module. The parameter setting module is used to set the necessary parameters for calculating the battery pack capacity. These parameters mainly include a charging data length threshold, an interruption threshold, and a reference capacity. The charging data length threshold is used to determine whether the charging data length is sufficient for capacity calculation. The interruption threshold is used to determine whether there is an interruption in the charging data. The reference capacity is used to find the best capacity estimate from multiple calculated capacity values. The charging data selection module refers to selecting current signals, voltage signals, time signals, and state of charge (SOC) signals from a large number of measured battery pack signals for battery pack capacity estimation; the basic calculation formula for battery pack capacity estimation is as follows: In the formula i k is the charging current at sampling point k, soc is the state of charge, time t is in seconds, and η is the coulombic efficiency, which is taken as 1 for lithium batteries. The charging data anomaly detection module is used to analyze the selected charging data and find the anomalies; the anomalies include data length anomalies, sampling anomalies, current anomalies, voltage anomalies, and SOC anomalies; the anomaly detection is to detect whether there are short data lengths, duplicate data, sampling interruptions, charging interruptions, or abnormal SOC growth. The abnormal charging data processing module is used to process abnormal data and eliminate its interference. For short data lengths, it uses a reference capacity as the optimal capacity estimate. For duplicate data, it removes duplicate data. For sampling and charging interruptions, if the interruption time exceeds a set threshold, it uses a segmented calculation method to find the optimal capacity estimate from the segmented calculated capacity based on the reference capacity. For intermittent charging, it identifies and removes data indicating charging interruptions. To address abnormal SOC growth, an interpolation method is used to estimate a reasonable SOC value. The optimal capacity estimation module is used to estimate a reasonable battery capacity value. For charging data without abnormalities, the battery pack capacity is directly calculated using formula (1) and the reference capacity is updated. For charging data with abnormalities, the battery pack capacity is calculated using the following formula in conjunction with the processing of abnormal data. In the formula, γ is the charging indicator factor. If charging is normal, let γ = 1, and if charging is interrupted, let γ = 0.

2. The method according to claim 1, characterized in that, Also includes: The reference capacity in the parameter setting module has an initial value determined by the initial capacity of the battery pack, and the reference capacity needs to be updated as the electric vehicle operates. The updated reference capacity is calculated based on the capacity obtained under the condition of no charging anomalies, and its update expression is as follows: in, For the updated reference capacity, C r C is the reference capacity before the update. max The capacity is calculated for a charging process without charging anomalies, with coefficient α∈[0,1].

3. The method according to claim 1, characterized in that, Also includes: The interrupt threshold in the parameter setting module needs to be determined according to the specific sampling process. The optimal value of the interrupt threshold is set to 10 to 20 times the normal sampling interval.

4. The method according to claim 1, characterized in that, Also includes: The charging data length threshold in the parameter setting module needs to be determined according to the specific sampling process. It is advisable to set the charging data length threshold to meet 10 minutes of charging.

5. The method according to claim 1, characterized in that, Also includes: The segmented calculation method involves dividing a charging process into several segments based on a set interruption threshold. If the length of a sub-segment is not less than a set length threshold, the capacity value of the corresponding segment is calculated. After calculating the capacity values ​​of all segments, the capacity value closest to the reference capacity is found as the estimated capacity of the charging process.

6. The method according to claim 1, characterized in that, Also includes: The aforementioned abnormal SOC growth is addressed by using interpolation to estimate a reasonable SOC value. This involves performing interpolation calculations on the SOC based on changes in charging voltage to estimate the SOC change over charging time.

7. The method according to claim 1, characterized in that, Also includes: The charging indicator factor γ is used to indicate whether the charging data has actually charged the battery; The charging indicator factor is set based on whether the SOC is increasing normally; if the change in SOC is negative, the charging indicator factor γ = 0 is set to 0, otherwise γ = 1; at the same time, if the following formula can satisfy... In the formula, ε is a small positive number, ΔT is the sampling interval, then let γ = 1 for the corresponding data, otherwise let γ = 0; the value of the coefficient ε needs to be determined according to the specific charging process.

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