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

Abstract

The invention 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, a charging data abnormity detection 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 is used for selecting signals for capacity estimation, the charging data anomaly detection module is used for finding anomalies in charging data, the abnormal charging data processing module is used for eliminating interference of abnormal data, and the optimal capacity estimation module is used for giving a reasonable capacity value. The method achieves the estimation of the capacity of the battery pack of the electric vehicle under the condition of abnormal charging through the special processing of the abnormal charging data, avoids the interference of the abnormal charging on an estimation result, improves the accuracy of the estimation of the capacity of the battery pack of the electric vehicle, and has a wide market application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a method for estimating the capacity of a battery pack under abnormal charging interference of an electric vehicle. Background Art

[0002] According to statistics, the transportation industry is the second largest source of greenhouse gas emissions in the world. Electric vehicles do not emit exhaust gas during operation, which is very beneficial to improving environmental quality. Due to their environmental friendliness and easy driving characteristics, as well as the continuous improvement of people's environmental awareness, electric vehicles are becoming more and more popular, and their market share is constantly increasing. As the core component of electric vehicles, batteries not only affect the price of electric vehicles, but are also the decisive factor affecting the safety and driving distance of electric vehicles.

[0003] Capacity is an important indicator of battery performance. It directly determines the driving distance and usage time of the vehicle. Battery capacity degradation during use is an unavoidable problem. From a safety perspective, the remaining capacity of the battery is usually required to be no less than 80% of the initial capacity. Therefore, accurate estimation of electric vehicle battery capacity is a very necessary technical issue.

[0004] At present, the commonly used battery capacity estimation methods include: direct measurement method, calculation method based on state of charge (SOC) and data-driven method. The data-driven method is a recently developed method, which is mainly based on the Internet of Things and artificial intelligence technology, especially deep learning technology. The data-driven method is to learn rules and patterns directly from the data, and then classify or predict the new data. However, this type of method still requires accurate capacity values ​​as sample labels. The coulomb counting method is simple and practical, and is widely used in practice.

[0005] Electric vehicles will encounter many abnormal situations during the charging process, such as abnormal data collection, intermittent charging, etc. Abnormal charging data will seriously affect the estimation of battery capacity, resulting in a large deviation in the battery capacity estimated by the existing technology. Summary of the invention

[0006] In view of the deficiencies of the existing technology in this field, the present invention provides a method for estimating the battery capacity of an electric vehicle under abnormal charging interference, so as to achieve accurate estimation of the battery capacity of the electric vehicle and improve the accuracy of the estimation of the battery capacity of the electric vehicle.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A method for estimating battery capacity under abnormal charging disturbance of an electric vehicle, comprising:

[0009] Parameter setting module, charging data selection module, charging data anomaly detection module, abnormal charging data processing module and optimal capacity estimation module. The parameter setting module is used to set the necessary parameters required to calculate the battery pack capacity, which mainly include charging data length threshold, interruption threshold and reference capacity; the charging data selection module refers to selecting current signals, voltage signals, time signals and SOC signals for battery pack capacity estimation from a large number of measured battery pack signals; the charging data anomaly detection module is used to analyze the selected charging data and find out the anomalies, which include data length anomalies, sampling anomalies, current anomalies, voltage anomalies and SOC anomalies; the abnormal charging data processing module is used to process abnormal data and eliminate the interference of abnormal data. The optimal capacity estimation module is used to calculate a reasonable battery pack capacity value.

[0010] Beneficial Effects

[0011] The method of the present invention can estimate the capacity of the electric vehicle battery pack under abnormal charging conditions by specially processing the abnormal charging data, thereby avoiding interference of the abnormal charging on the estimation result. Compared with the existing methods, the method of the present invention has reasonable estimation results and has broad market application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic flow chart of a method for estimating battery pack capacity under abnormal charging interference of an electric vehicle provided by an embodiment of the present invention;

[0013] Figure 2 A schematic diagram of a method for estimating battery capacity under abnormal charging interference of an electric vehicle provided by an embodiment of the present invention;

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

[0015] Figure 4 Another example of abnormal charging of an electric vehicle provided by an embodiment of the present invention;

[0016] Figure 5 Another example of abnormal charging of an electric vehicle is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is described in detail below in conjunction with the accompanying drawings. It should be pointed out that the following description is for a better understanding of the process of the present invention, but does not have a restrictive effect on the present invention.

[0018] The present invention provides a method for estimating the battery capacity of an electric vehicle 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. Figure 1 shown.

[0019] The parameter setting module is used to set the necessary parameters required for calculating the battery pack capacity, and 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 calculating the capacity, and the charging data length threshold needs to be determined according to the specific sampling process, and the charging data length threshold is preferably set to meet 10 minutes of charging; the interruption threshold is used to determine whether there is an interruption in the charging data, and 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 estimation value from multiple calculated capacity values, and 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 runs.

[0020] The charging data selection module is to select the current signal, voltage signal, time signal and SOC signal for battery pack capacity estimation from the measured battery pack signals; the basic calculation formula of the battery pack capacity estimation is:

[0021]

[0022] Where i(·) is the charging current, soc is the state of charge, time is in seconds, η is the coulomb efficiency, and for lithium batteries, η=1.

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

[0024] The abnormal charging data processing module is used to process abnormal data and eliminate the interference of abnormal data; to address the problem of short data length, use the reference capacity as the optimal capacity estimation value; to address the problem of data duplication, remove the duplicate data; to address the problem of sampling interruption and charging interruption, if the interruption time exceeds the set threshold, use the segmented calculation method to find the optimal capacity estimation value from the segmented calculated capacity based on the reference capacity; to address the intermittent charging, find the data points of charging interruption and remove them; to address the abnormal SOC growth, use the interpolation method to estimate a reasonable SOC value.

[0025] The segmented calculation method is to divide a charging process into several segments according to the set interruption threshold. If the length of the sub-segment is not less than the 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 value of the capacity of the charging process.

[0026] The abnormal SOC growth described above can be estimated using the interpolation method to obtain a reasonable SOC value, that is, the SOC is interpolated according to the change of the charging voltage to estimate the value of the SOC changing with the 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 combination with the processing of the abnormal data:

[0028]

[0029] Where γ is the 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 calculated without abnormal charging, and its updated expression is

[0030]

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

[0032] The charging indication factor γ is used to indicate whether the charging data actually charges the battery. The charging indication factor is set according to whether the SOC increases normally. If the change in SOC is a negative value, the charging indication factor γ corresponding to the data is set to 0, otherwise γ is set to 1. At the same time, if the following formula can be satisfied

[0033]

[0034] In the formula, ε is a small positive number, ΔT is the sampling interval, and γ corresponding to the data is set to 1, otherwise γ is set to 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. The structural schematic diagram of the method of the present invention is as follows: Figure 2 The specific implementation process of the present invention is described in detail below.

[0037] First, set the necessary parameters required to calculate the battery pack capacity. These parameters mainly include the charging data length threshold, interruption threshold and reference capacity. The charging data length threshold is used to determine whether the charging data length is sufficient for calculating the capacity; 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.

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

[0039] Anomaly detection is performed on the data of a complete charging process to detect 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, the 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 optimal capacity estimation value. If there is a duplication problem in the data, the duplicate data is eliminated. 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 indication factor γ of the segment whose length is not less than the length threshold is calculated. If it is found that the battery capacity has decreased, the charging indication factor γ corresponding to this data is set to 0, otherwise it is set to γ ​​= 1, and the battery pack capacity of the segment whose length is not less than the length threshold is calculated using formula (2), and then the optimal capacity estimation value is found from the segmented calculated capacity based on the reference capacity. If the SOC growth is abnormal, the interpolation method is used to estimate a reasonable SOC value; if charging is intermittent, the charging interruption data is found and eliminated, that is, the charging indication factor γ corresponding to the data point is set to 0, the charging indication factor is calculated, and the battery pack capacity is calculated using formula (2).

[0041] On this basis, capacity calculation is performed 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 formula (2) in combination with the processing of the abnormal data.

[0042] When the charging data length threshold is set to 70, the sampling interruption threshold is set to 100s, the initial reference capacity is set to 135, α = 0.7, ε = 0.2, the comparison results of a group of electric vehicle battery pack capacity estimation are shown in Figure 2. Figure 3-Figure 5 The above parameters need to be determined according to the specific charging process, and the examples provided here are for reference only.

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

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

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

[0046] Although the above is a description of the implementation steps of the present invention in conjunction with the accompanying drawings, it is not intended to limit the present invention. Any modifications made on the basis of the scheme of the present invention are within the protection scope of the present invention.

Claims

1. A method for estimating battery capacity under abnormal charging disturbance of an electric vehicle, characterized in that: include: Parameter setting module, charging data selection module, charging data anomaly detection module, abnormal charging data processing module and optimal capacity estimation module; The parameter setting module is used to set the necessary parameters required for calculating the battery pack capacity, and 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 calculating the capacity; 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 estimation value from multiple calculated capacity values; The charging data selection module is to select the current signal, voltage signal, time signal and state of charge (SOC) signal for battery pack capacity estimation from the measured battery pack signals; the basic calculation formula for battery pack capacity estimation is: Where i(·) is the charging current, soc is the state of charge, the time t is in seconds, η is the coulomb efficiency, and for lithium batteries, η=1; The charging data anomaly detection module is used to analyze the selected charging data to 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 is short data length, data duplication, sampling interruption, charging interruption (including intermittent), SOC growth anomaly; The abnormal charging data processing module is used to process abnormal data and eliminate the interference of abnormal data; for the problem of short data length, the reference capacity is used as the best capacity estimation value; for the problem of data duplication, the duplicate data is eliminated; for the problem of sampling interruption and charging interruption, if the interruption time exceeds the set threshold, the segmented calculation method is used to find the best capacity estimation value from the segmented calculated capacity according to the reference capacity; for the intermittent charging, the charging interruption data is found and eliminated; In view of the abnormal SOC growth, the interpolation method is used to estimate the 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 combination with the processing of the abnormal data: Where 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, 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 runs; The calculation of the updated reference capacity is based on the capacity calculated without charging anomalies, and its updated expression is: in, is the updated reference capacity, C r is the reference capacity before updating, C max The capacity is calculated for the charging process without charging anomalies, and the coefficient α∈[0,1].

3. The method according to claim 1, characterized in that Also includes: The interruption threshold in the parameter setting module 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.

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, and it is appropriate 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 is to divide a charging process into several segments according to the set interruption threshold. If the length of the sub-segment is not less than the 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 value of the capacity of the charging process.

6. The method according to claim 1, characterized in that Also includes: The abnormal SOC growth described above can be estimated using the interpolation method to obtain a reasonable SOC value, that is, the SOC is interpolated according to the change of the charging voltage to estimate the value of the SOC changing with the charging time.

7. The method according to claim 1, characterized in that Also includes: The charging indication factor γ is used to indicate whether the charging data actually charges the battery; The charging indicator factor is set according to whether the SOC increases normally; if the change in SOC is a negative value, the charging indicator factor γ corresponding to the data is set to 0, otherwise γ is set to 1; at the same time, if the following formula can be satisfied In the formula, ε is a small positive number, ΔT is the sampling interval, and γ corresponding to the data is set to 1, otherwise γ is set to 0; the value of the coefficient ε needs to be determined according to the specific charging process. In the embodiment of the present invention, ε=0.2 is taken for reference.

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