Maximum capacity obtaining method based on PHEV power battery real vehicle data and computer equipment

Abstract

The invention relates to the technical field of automobile power batteries, in particular to a maximum capacity obtaining method based on PHEV power battery real vehicle data and computer equipment, and the method comprises the following steps: extracting real vehicle SOC data and OCV data according to real vehicle BMS data, and fitting an OCV-SOC curve according to the OCV data and the SOC data; s100, selecting a working section of the battery, calculating OCV data of the working section through real vehicle SOC data and the OCV-SOC curve obtained in the step S100, and further calculating the internal resistance of the battery of the working section; according to the OCV-SOC curve and the internal resistance of the battery obtained in the step S200, the terminal voltage Ut (t) of the working section is expressed as # imgabs0 #, and the initial SOC value SOCt0 and the reciprocal K1 of the maximum available capacity in the formula are obtained in a least square optimization mode through the terminal voltage and current data of the working section. The method can be used for a relatively short charging and discharging working section in the PHEV so as to realize real-time estimation of the maximum available capacity of the power battery of the vehicle and further judge the service life attenuation condition of the power battery of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive power batteries, and more specifically, to a method for obtaining the maximum capacity based on real vehicle data of a PHEV power battery and a computer device. Background Art

[0002] As the power source of new energy vehicles, power batteries fundamentally determine the performance and service life of new energy vehicles. Due to their high specific capacity, high energy density and long service life, lithium-ion batteries are widely used as power batteries for new energy vehicles. However, during the use of lithium-ion batteries, as they are continuously charged and discharged, the batteries will definitely age, their maximum available capacity will decrease, and the internal resistance of the batteries will increase. Therefore, during the use of automotive power batteries, it is necessary to monitor the aging and attenuation of the batteries in real time to ensure their safety and stable energy output. This is also one of the core functions of the battery management system (BMS).

[0003] The battery health state (SOH) is used to represent the life decay of lithium-ion batteries. The battery health state is generally represented by the ratio of the current maximum available capacity of the battery to the maximum available capacity of the battery at the initial moment. When the health state of the vehicle power battery drops to 80%, it is considered that the battery can no longer be used in electric vehicles and needs to be replaced. Therefore, the most critical thing to obtain the battery health state is to calculate the maximum available capacity of the current battery. Many researchers have conducted relevant research on the calculation of the maximum available capacity and proposed corresponding calculation methods. The currently commonly used maximum available capacity calculation methods can be roughly divided into two categories. One is the experimental analysis method, including the ampere-hour integration method and the differential voltage analysis method, and the other is the model-based method, including the Kalman filter estimation method and the machine learning modeling method.

[0004] For the above-mentioned maximum available capacity calculation method, relevant researchers have verified its feasibility through experimental battery data, but the above-mentioned method is difficult to be directly used for the calculation of the maximum available capacity of the actual vehicle power battery, especially the PHEV power battery with less parking and charging. First of all, for the ampere-hour integration method, it requires high-precision battery current and battery state of charge (SOC) data to ensure the calculation accuracy of the maximum available capacity, which can be obtained by measuring related equipment in the laboratory. However, for the actual application scenarios of power batteries, it is difficult to obtain accurate battery current and SOC data due to the accuracy of BMS sensors and data storage limitations. At this time, it is necessary to have parking charging data or discharge data with large SOC changes in the vehicle battery data to calculate the maximum available capacity, which has reduced the error caused by low data accuracy, which is easier to achieve on EVs. However, for PHEVs, since it can use batteries to drive motors to power the vehicle, or the engine can power the vehicle, due to differences in user habits, there will be many vehicles with fewer or shorter parking and charging sections, and it is difficult to directly apply the ampere-hour integration method to calculate the maximum available capacity. Secondly, for the differential voltage analysis method, the maximum available capacity is calculated by comparing the relationship between the differential voltage and the maximum available capacity attenuation during the charging process. This requires a charging segment with stable length and current during each charge and discharge cycle. This is easy to achieve in the experimental test process, but for the actual application of power batteries, it is difficult to guarantee that users will have a stable charging mode. Therefore, it is difficult to directly use it for the calculation of the maximum available capacity of the actual vehicle power battery.

[0005] For the Kalman filter estimation method, an equivalent circuit model is used to approximate the relationship between the battery's current and terminal voltage, and then the obtained physical equation is placed in the Kalman filter framework to identify the maximum available capacity. In the process of actual vehicle application, due to the low accuracy and large error of the actual SOC data, the actual collected value cannot be directly input into the filter, and then two coupled filters are required to simultaneously identify the SOC and the maximum available capacity. However, since the appropriate state noise covariance and observation noise covariance need to be input during the operation of the Kalman filter, and due to the complexity of the error during the actual vehicle operation, it is difficult to directly calculate the noise covariance, so there are 4 variables that need to be calibrated during the actual operation of the algorithm, which will greatly increase the computational cost. For the machine learning modeling method, it is first necessary to obtain the maximum available capacity of the same type of new energy vehicle power battery in different periods, and then input it as a label into the model for training, so that the trained model can be used to predict the maximum available capacity. Therefore, if the maximum available capacity change of the power battery of a certain type of new energy vehicle is completely unknown, the machine learning modeling method cannot be used directly. A suitable maximum available capacity direct calculation method is required to calculate the maximum available capacity change of this type of vehicle under different working conditions. Only after the model training is completed can the machine learning model be used to predict the change in the maximum available capacity. In addition, if only experimental battery data is used to train the machine learning model, since it is difficult for the laboratory to fully simulate the operating scenario of the actual vehicle, the model trained using the data may not be able to obtain the operating characteristics of the actual vehicle, which in turn leads to a large deviation when the model is used to predict the maximum available capacity of the actual vehicle battery.

[0006] The above commonly used maximum available capacity calculation method is mostly applicable to experimental battery data and EV power battery data with long parking charging or discharging segments. However, for PHEV power battery data, due to the existence of the hybrid charging mode, its parking charging segment and pure discharging segment will be relatively few and short. It is difficult to calculate the maximum available capacity of the power battery in real time using the currently commonly used maximum available capacity calculation method. Summary of the invention

[0007] The purpose of the present invention is to overcome the problem that the currently commonly used maximum available capacity calculation method in the prior art is difficult to calculate the maximum available capacity of a PHEV power battery in real time, and to provide a maximum capacity acquisition method and computer equipment based on PHEV power battery real vehicle data, which can calculate the maximum available capacity of a PHEV power battery in real time.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for obtaining the maximum capacity based on the actual vehicle data of the PHEV power battery, comprising the following steps:

[0009] S100: extracting the SOC data, current value and OCV data of the actual vehicle according to the BMS data of the actual vehicle, and fitting the OCV-SOC curve according to the OCV data and SOC data;

[0010] S200: Select a working section of the battery, calculate the OCV data of the working section through the actual vehicle SOC data and the OCV-SOC curve obtained in step S100, and then calculate the battery internal resistance of the working section;

[0011] S300: Based on the OCV-SOC curve and the battery internal resistance obtained in step S200, the terminal voltage U t (t) is expressed as: Using the terminal voltage and current data of the working section, the initial SOC value SOC in the formula is obtained through the least squares optimization method. t0 And the inverse of the maximum available capacity K1, where R represents the internal resistance of the battery and I(t) represents the current value in the actual vehicle BMS data.

[0012] In the technical solution of the present application, through the above-mentioned steps, based on the approximate results of the OCV-SOC curve of the automotive power battery, the problem of difficulty in obtaining the OCV of the actual vehicle power battery is solved. The present invention can use shorter actual vehicle power battery charge and discharge data to achieve maximum available capacity estimation, and then realize the monitoring of PHEV power battery life attenuation.

[0013] Preferably, in step S300, the step of calculating the inverse K1 of the maximum available capacity is specifically as follows: S301: The terminal voltage calculation formula in step S300 is transformed in the following manner:

[0014]

[0015] S302: Use the least squares method to calculate the initial value SOC of the unknown parameter SOC t0 And the inverse of the maximum available capacity K1, perform optimization estimation, the specific method is as follows:

[0016] First, express the above formula as:

[0017]

[0018] in represents the inverse function of the OCV-SOC curve extracted in step S100, and let:

[0019]

[0020] Then we have:

[0021] y i =x i θ

[0022] Assuming that the selected working section uses a total of n counting points, let:

[0023]

[0024] From this we can get:

[0025] Y=Xθ

[0026] S303: According to the calculation formula of the least square method, the optimization result of the parameter vector θ is:

[0027] θ=(X T X) -1 X T Y

[0028] S304: Taking the inverse of K1 in the parameter vector θ, the maximum available capacity of the current battery can be obtained.

[0029] Preferably, the step S100 includes the following steps:

[0030] S101: For the acquired real vehicle BMS data, first, calculate the time difference between each recording point and the previous recording point, take the recording point with a time difference greater than t as the OCV recording point, and record the OCV data and the corresponding SOC data of the OCV recording point;

[0031] S102: Using the KNN algorithm, a binary classification unsupervised learning model is built to screen and remove abnormal values ​​in the OCV recording points;

[0032] S103: Fitting an OCV-SOC curve according to the OCV recording points after cleaning the abnormal values, and obtaining an actual vehicle OCV-SOC curve.

[0033] Preferably, the step S200 specifically includes the following steps:

[0034] S201: For the SOC data collected by the real vehicle BMS, firstly, the recording points with the same SOC data value are linearly split;

[0035] S202: Input the split SOC data into the extracted OCV-SOC curve to obtain the OCV of each recording point in the working section;

[0036] S203: After obtaining the OCV of each recording point in the working section, the battery internal resistance at each recording point is calculated using the following formula:

[0037]

[0038] Among them U OCV(t) represents the OCV data of the recording point, and I(t) represents the current value of the recording point.

[0039] Preferably, the step S200 further comprises a step S204, wherein a signal smoothing method of empirical mode decomposition or moving average method is used to obtain a smooth trend line for the calculated battery resistance, and the trend line is used as the internal resistance of the working section.

[0040] Preferably, in step S204, the empirical mode decomposition can be implemented by the PyEMD library in Python language or the emd function in Matlab, and the calculated battery internal resistance sequence is input into the function to return the decomposition result of the sequence.

[0041] Preferably, in step S204, the moving average method can be implemented by the rolling function of the Pandas library in the Python language or the movmean function in Matlab. After the calculated battery internal resistance sequence is input into the function, the moving average smoothing result of the resistance can be returned.

[0042] Preferably, the time difference t is in the range of 3600s<t.

[0043] Preferably, in step S200, the working section is a charging section or a discharging section of the battery.

[0044] Another aspect of the present invention provides a computer device, comprising a storage device and a processor, wherein the storage device stores a computer program, and wherein the processor implements the steps of the method described above when executing the computer program.

[0045] The maximum capacity acquisition method based on the actual vehicle data of PHEV power batteries in the present invention proposes an approximate result of the OCV-SOC curve of the vehicle power battery obtained through actual vehicle data, which solves the problem that the OCV of the actual vehicle power battery is difficult to obtain. The present invention can use shorter actual vehicle power battery charge and discharge data to realize the maximum available capacity estimation, and then realize the monitoring of the life attenuation of the PHEV power battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A graph showing the relationship between accumulated mileage and running time in Example 2 of the maximum capacity acquisition method based on actual vehicle data of a PHEV power battery of the present invention;

[0047] Figure 2 A voltage-SOC relationship diagram of the OCV recording point extracted in Example 2 of the maximum capacity acquisition method based on PHEV power battery real vehicle data of the present invention;

[0048] Figure 3This is a diagram showing the results of abnormal point screening in Example 2 of the maximum capacity acquisition method based on PHEV power battery real vehicle data of the present invention;

[0049] Figure 4 It is a real vehicle OCV-SOC curve diagram in Example 2 of the maximum capacity acquisition method based on real vehicle data of PHEV power battery of the present invention;

[0050] Figure 5 (a) is a SOC linear decomposition diagram in Example 2 of the maximum capacity acquisition method based on PHEV power battery real vehicle data of the present invention; Figure 5 (b) is the OCV diagram of the working section obtained by inputting the linear split SOC in Example 2 of the maximum capacity acquisition method based on the actual vehicle data of the PHEV power battery of the present invention;

[0051] Figure 6 (a) is a diagram showing the battery resistance calculation results in Example 2 of the maximum capacity acquisition method based on PHEV power battery real vehicle data of the present invention; Figure 6 (b) is the resistance trend line extracted by empirical mode decomposition in Example 2 of the maximum capacity acquisition method based on PHEV power battery real vehicle data of the present invention;

[0052] Figure 7 (a) is the calculation result of the maximum available capacity of a single ampere-hour integral; Figure 7 (b) is a single ampere-hour points settlement result diagram of Example 2 of the maximum capacity acquisition method based on PHEV power battery real vehicle data of the present invention;

[0053] Figure 8 This is a flow chart of Example 1 of a method for obtaining the maximum capacity based on actual vehicle data of a PHEV power battery according to the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with specific implementation modes.

[0055] Example 1

[0056] like Figure 8 As shown, a method for obtaining the maximum capacity based on the actual vehicle data of a PHEV power battery comprises the following steps:

[0057] S100: extracting SOC data and OCV data of the actual vehicle according to the BMS data of the actual vehicle, and fitting an OCV-SOC curve according to the OCV data and the SOC data;

[0058] S200: Select a working section of the battery, calculate the OCV data of the working section through the actual vehicle SOC data and the OCV-SOC curve obtained in step S100, and then calculate the battery internal resistance of the working section;

[0059] S300: Based on the OCV-SOC curve and the battery internal resistance obtained in step S200, the terminal voltage U t (t) is expressed as: Using the terminal voltage and current data of the working section, the initial SOC value SOC in the formula is obtained through the least squares optimization method. t0 And the reciprocal of the maximum available capacity K1, where R represents the internal resistance of the battery and I(t) represents the current value.

[0060] In this embodiment, since the traditional maximum available capacity calculation method requires experimental battery data or the battery has relatively complete charge and discharge data during use, this is often easy to achieve on EVs. However, due to the characteristic that PHEVs may not be powered by battery motors and differences in user habits, there may be a lack of long charging segments in the PHEV power battery data. The maximum capacity acquisition method based on the actual vehicle data of PHEV power batteries proposed in this embodiment can use shorter actual vehicle power battery charge and discharge data to achieve the maximum available capacity estimation, and then realize the monitoring of the life attenuation of PHEV power batteries. Generally speaking, the OCV-SOC curve acquisition of lithium-ion batteries requires the use of specific experimental equipment for measurement, and for new energy vehicles that have been put on the market, it is difficult to meet this condition. This method proposes to obtain the approximate result of the OCV-SOC curve of the automotive power battery through actual vehicle data, which solves the problem that the OCV of the actual vehicle power battery is difficult to obtain.

[0061] Among them, in the step S300, the step of calculating the inverse K1 of the maximum available capacity is specifically as follows: S301: For the terminal voltage calculation formula in step S300, the following method is used to transform it:

[0062]

[0063] S302: Use the least squares method to calculate the initial value SOC of the unknown parameter SOC t0 And the inverse of the maximum available capacity K1, perform optimization estimation, the specific method is as follows:

[0064] First, express the above formula as:

[0065]

[0066] in represents the inverse function of the OCV-SOC curve extracted in step S100, and let:

[0067]

[0068] Then we have:

[0069] y i =x iθ

[0070] Assuming that the selected working section uses a total of n counting points, let:

[0071]

[0072] From this we can get:

[0073] Y=Xθ

[0074] S303: According to the calculation formula of the least square method, the optimization result of the parameter vector θ is:

[0075] θ=(X T X) -1 X T Y

[0076] S304: Taking the inverse of K1 in the parameter vector θ, the maximum available capacity of the current battery can be obtained.

[0077] Wherein, the step S100 includes the following steps:

[0078] S101: For the acquired real vehicle BMS data, first, calculate the time difference between each recording point and the previous recording point, take the recording point with a time difference greater than t as the OCV recording point, and record the OCV data and the corresponding SOC data of the OCV recording point;

[0079] S102: Using the KNN algorithm, a binary classification unsupervised learning model is built to screen and remove abnormal values ​​in the OCV recording points;

[0080] S103: Fitting an OCV-SOC curve according to the OCV recording points after cleaning the abnormal values, and obtaining an actual vehicle OCV-SOC curve.

[0081] In addition, the step S200 specifically includes the following steps:

[0082] S201: For the SOC data collected by the real vehicle BMS, its accuracy is generally low, and it is common for multiple recording points to have the same SOC value. Therefore, the recording points with the same SOC data value must first be linearly split; S202: The split SOC data is input into the extracted OCV-SOC curve to obtain the OCV of each recording point in the working section;

[0083] S203: After obtaining the OCV of each recording point in the working section, the battery internal resistance at each recording point is calculated using the following formula:

[0084]

[0085] Among them U OCV(t) represents the OCV data of the recording point, and I(t) represents the current value of the recording point, which is the current value in the actual vehicle BMS data.

[0086] Wherein, the step S200 also includes step S204. For the calculated battery resistance, due to the linear decomposition of SOC and the measurement error of the actual vehicle measured data, the calculation result usually fluctuates to a certain extent. The signal smoothing method of empirical mode decomposition or moving average method is used to obtain a smooth trend line, and the trend line is used as the internal resistance of the working section.

[0087] In addition, in step S204, the empirical mode decomposition can be implemented by the PyEMD library in Python language or the emd function in Matlab, and the calculated battery internal resistance sequence is input into the function to return the decomposition result of the sequence.

[0088] Among them, in the step S204, the moving average method can be implemented by the rolling function of the Pandas library in the Python language or the movmean function in Matlab. After the calculated battery internal resistance sequence is input into the function, the moving average smoothing result of the resistance can be returned.

[0089] In addition, the range of the time difference t is 3600s<t.

[0090] Wherein, in the step S200, the working section is a charging section or a discharging section of the battery.

[0091] Example 2

[0092] like Figures 1 to 7 As shown, this embodiment provides a method for obtaining the maximum capacity of a PHEV power battery based on actual vehicle data. The actual battery data measured by the BMS system in the PHEV can be used to estimate the maximum available capacity of the current power battery using the short parking charging data or short discharge data therein. The actual vehicle battery data of a GAC ​​Trumpchi GS4 new energy vehicle is used to verify the present invention.

[0093] The starting running time of the vehicle data is 2018.8 and the ending running time is 2021.6. Figure 1 As shown in the figure, the relationship between the accumulated mileage and the running time of the vehicle is shown. According to the steps described above, the time difference between each recording point and the previous recording point in the vehicle data is first calculated, and the recording point with a time difference greater than 3600s is selected as the OCV recording point of the vehicle's power battery. The relationship between the voltage and SOC of the extracted OCV recording point is shown in Figure 2 As shown in the figure, for the extracted OCV recording points, due to the measurement errors in the actual vehicle data and the large current that may exist in the extracted OCV recording points, Figure 2 Therefore, the KNN algorithm is used to build an unsupervised binary classification model to filter out scattered abnormal points and remove them. The screening results are as follows: Figure 3 Then, after removing the abnormal OCV recording points, the relationship between voltage and SOC data was fitted using a quintic polynomial to obtain the OCV-SOC curve extracted based on the actual vehicle, as shown in Figure 4 shown.

[0094] Then, select the appropriate charge and discharge segment, and obtain the OCV and internal resistance of the working segment based on the SOC data and the extracted real vehicle OCV-SOC curve. Since the recording accuracy of SOC in the real vehicle data is 1%, there will be multiple recording points with the same SOC data in a working segment. Therefore, the SOC of the selected working segment is first linearly split, and then input into the extracted real vehicle OCV-SOC curve to obtain the OCV data of the working segment. Figure 5 (a) and 5(b) show the SOC linear splitting result and OCV acquisition result of the vehicle in a charging section. Afterwards, the battery resistance at each recording point in the working section is calculated using the following formula:

[0095]

[0096] The calculation results are as follows Figure 6 As shown in (a), due to the measurement error in the actual vehicle data and the error generated in the SOC linear decomposition process, the calculated resistance will fluctuate to a certain extent. Therefore, empirical mode decomposition is used to obtain the trend line of resistance change, as shown in Figure 6 (b) This trend line is used for subsequent capacity estimation.

[0097] Finally, use the following formula to estimate the current maximum available capacity of the battery:

[0098]

[0099] Among them, the unknown parameter is the initial SOC value SOC t0 And the inverse of the maximum available capacity K1, use the least squares method to optimize and estimate, and you can get the maximum available capacity of the current battery. Figure 7 (a) and (b) are the current maximum available capacity of the battery calculated using the single ampere-hour integration method and the method of the present invention, respectively. When using the single ampere-hour integration method, the SOC change value of the working section needs to be greater than 20%, while using the current method, for the vehicle data, the length of the working section only needs to be greater than 60 recording points (SOC change is about 4-6%). This algorithm has better applicability than the single ampere-hour integration method.

[0100] Example 3

[0101] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0102] In the specific contents of the above-mentioned specific implementation methods, the various technical features can be combined in any non-contradictory manner. In order to make the description concise, not all possible combinations of the above-mentioned technical features are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for obtaining the maximum capacity of a PHEV power battery based on actual vehicle data, characterized in that: The following steps are involved: S100: extracting the SOC data and OCV data of the actual vehicle according to the BMS data of the actual vehicle, and fitting the OCV-SOC curve according to the OCV data and the SOC data; S200: Select a working section of the battery, calculate the OCV data of the working section through the actual vehicle SOC data and the OCV-SOC curve obtained in step S100, and then calculate the battery internal resistance of the working section; S300: Based on the OCV-SOC curve and the battery internal resistance obtained in step S200, the terminal voltage U t (t) is expressed as: Using the terminal voltage and current data of the working section, the initial SOC value SOC in the formula is obtained through the least squares optimization method. t0 And the inverse of the maximum available capacity K1, where R represents the internal resistance of the battery and I(t) represents the current value in the actual vehicle BMS data.

2. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to claim 1 is characterized in that: In step S300, the steps of calculating the inverse K1 of the maximum available capacity are specifically as follows: S301: The terminal voltage calculation formula in step S300 is transformed in the following manner: S302: Use the least squares method to calculate the initial value SOC of the unknown parameter SOC t0 And the inverse of the maximum available capacity K1, perform optimization estimation, the specific method is as follows: First, express the above formula as: in represents the inverse function of the OCV-SOC curve extracted in step S100, and let: Then we have: y i =x i i Assuming that the selected working section uses a total of n counting points, let: From this we can get: Y=Xθ S303: According to the calculation formula of the least square method, the optimization result of the parameter vector θ is: θ=(X T X) -1 X T Y S304: Taking the inverse of K1 in the parameter vector θ, the maximum available capacity of the current battery can be obtained.

3. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to claim 1 is characterized in that: The step S100 includes the following steps: S101: For the acquired real vehicle BMS data, first, calculate the time difference between each recording point and the previous recording point, take the recording point with a time difference greater than t as the OCV recording point, and record the OCV data and the corresponding SOC data of the OCV recording point; S102: Using the KNN algorithm, a binary classification unsupervised learning model is built to screen and remove abnormal values ​​in the OCV recording points; S103: Fitting an OCV-SOC curve according to the OCV recording points after cleaning the abnormal values, and obtaining an actual vehicle OCV-SOC curve.

4. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to claim 1 is characterized in that: The step S200 specifically includes the following steps: S201: For the SOC data collected by the real vehicle BMS, firstly, the recording points with the same SOC data value are linearly split; S202: Input the split SOC data into the extracted OCV-SOC curve to obtain the OCV of each recording point in the working section; S203: After obtaining the OCV of each recording point in the working section, the battery internal resistance at each recording point is calculated using the following formula: Among them U OCV (t) represents the OCV data of the recording point, and I(t) represents the current value of the recording point, which is the current value in the actual vehicle BMS data.

5. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to claim 4 is characterized in that: The step S200 further includes a step S204, for obtaining a smoothed trend line for the calculated battery resistance using a signal smoothing method such as empirical mode decomposition or a moving average method, and using the trend line as the internal resistance of the working section.

6. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to claim 5 is characterized in that: In step S204, the empirical mode decomposition can be implemented by using the PyEMD library in Python or the emd function in Matlab, and the calculated battery internal resistance sequence is input into the function to return the decomposition result of the sequence.

7. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to claim 5 is characterized in that: In step S204, the moving average method can be implemented by the rolling function of the Pandas library in Python or the movmean function in Matlab. After the calculated battery internal resistance sequence is input into the function, the moving average smoothing result of the resistance can be returned.

8. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to claim is characterized in that: The range of the time difference t is 3600s<t.

9. The maximum capacity acquisition method based on PHEV power battery real vehicle data according to any one of claims 1 to 8, characterized in that: In the step S200, the working section is a charging section or a discharging section of the battery.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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