Power battery charging curve dynamic prediction method based on deviation adjustment

Abstract

The invention relates to the technical field of power batteries, and discloses a power battery charging curve dynamic prediction method and system based on deviation adjustment, and the method comprises the steps: obtaining the historical charging data of a vehicle; determining charging mode classifications, ideal charging curves corresponding to the classifications and historical charging fragment feature data of the classifications according to the historical charging data of the vehicle, and constructing a data set; training the classification model by using the data set to obtain a trained classification model; through the trained classification model, obtaining predicted charging mode classification according to the real-time charging segment feature data, and obtaining a corresponding ideal charging curve as an initial prediction curve; and calculating a deviation adjustment item through a deviation adjustment formula according to the real-time charging segment feature data, and adjusting the initial prediction curve based on the deviation adjustment item to obtain a corrected prediction curve. According to the method, the charging duration can be accurately predicted, the charging process is dynamically optimized, the charging time is shortened, and the utilization efficiency of the charging pile and the vehicle-mounted power battery is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power batteries, and in particular to a method for dynamically predicting a charging curve of a power battery based on deviation adjustment. Background Art

[0002] At present, most of the literature on the prediction of charging time of power batteries for electric vehicles analyzes from the perspective of charging outlets, focusing on the allocation of charging station resources, grid load balancing, and optimization of user charging experience. However, these studies are mainly aimed at public charging outlets with a high degree of intelligence. Such charging facilities are usually concentrated in areas with well-equipped infrastructure such as large cities or highway service areas. For vehicles that need to travel frequently across regions, relying solely on specific charging outlets for prediction has great limitations. If the vehicle can autonomously predict its charging time based on the real-time charging data of the local power battery without being limited to the charging outlet, it can not only help the driver plan the trip reasonably and reduce unnecessary waiting time, but also significantly improve travel efficiency and user experience, and achieve more intelligent charging behavior management.

[0003] The problem of predicting the charging time of a vehicle can actually be regarded as a collection of the problem of charging mode classification and the problem of automatic fitting of the charging curve. First of all, the morphology of the charging curve is highly similar under different charging modes (such as constant current mode and constant voltage mode). Therefore, classification for specific charging modes is a prerequisite for accurate prediction of the charging time of power batteries. Secondly, from the actual use scenarios of new energy vehicles, real-time prediction of charging time still faces many challenges. The main reason is that during the charging process, due to the combined influence of factors such as the current battery temperature, SOC and charging rate, the charging power is prone to dynamic adjustment or fluctuation, resulting in the staged irregular characteristics of the charging curve. These problems directly affect the accuracy of the charging time prediction. Therefore, in the charging process, in order to effectively cope with the dynamic fluctuations of current and voltage, proposing a corresponding efficient and adaptive deviation adjustment model is another prerequisite for accurate prediction of battery charging time.

[0004] Therefore, there is an urgent need for a dynamic prediction method for power battery charging curve based on deviation adjustment, which can accurately predict the charging time and dynamically optimize the charging process, reduce the charging time, and improve the utilization efficiency of charging piles and on-board power batteries. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a dynamic prediction method for a power battery charging curve based on deviation adjustment, which can accurately predict the charging time and dynamically optimize the charging process, reduce the charging time, and improve the utilization efficiency of charging piles and vehicle-mounted power batteries.

[0006] The present invention provides a method for dynamically predicting a power battery charging curve based on deviation adjustment, comprising the following steps:

[0007] S1. Obtaining historical charging data of the vehicle; wherein the historical charging data includes time, voltage, current, temperature and SOC value;

[0008] S2. Determine the charging mode classification, the ideal charging curve corresponding to each classification, and the historical charging segment feature data of each classification according to the historical charging data of the vehicle, and construct a data set according to the charging mode classification and the historical charging segment feature data of each classification;

[0009] S3, build a classification model, use the data set to train the classification model, and obtain a trained classification model;

[0010] S4, obtaining real-time charging segment feature data, obtaining a predicted charging mode classification through a trained classification model, and obtaining a corresponding ideal charging curve as an initial prediction curve according to the predicted charging mode classification;

[0011] S5. Calculate a deviation adjustment item through a deviation adjustment formula according to the real-time charging segment characteristic data, and adjust the initial prediction curve based on the deviation adjustment item to obtain a corrected prediction curve.

[0012] Furthermore, in S3, the classification model is an LSTM model, the input of the classification model is the historical charging segment feature data, and the output of the classification model is the charging mode classification corresponding to the historical charging segment feature data.

[0013] Further, in S5, the deviation adjustment item is calculated by the deviation adjustment formula according to the real-time charging segment characteristic data, and the initial prediction curve is adjusted based on the deviation adjustment item to obtain a corrected prediction curve, wherein the deviation adjustment formula is as follows:

[0014] s i+1 =s i +ωc i Δt+δ=f(t,c i Δt,s i ,σ);

[0015] Among them, s i Indicates the SOC value at the current moment, s i+1 represents the SOC value at the next moment, c i represents the current at the current moment, △t represents the sampling time interval, δ represents the deviation adjustment term, σ represents the statistical value of the static characteristic, ω represents the coefficient of the linear relationship between the current and the SOC value, f(t, c i Δt, s i, σ) represents the coefficient of the linear relationship between current and SOC value and the relationship between current and static characteristics, and t represents time t; wherein, the deviation adjustment item is divided into linear deviation adjustment item, logarithmic deviation adjustment item, proportional deviation adjustment item and deviation adjustment item based on deep learning model.

[0016] Furthermore, when the deviation adjustment item is a linear deviation adjustment item, the calculation formula of the deviation adjustment item is as follows:

[0017]

[0018] Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, represents the expected value of the predicted current, and k represents the adjustment coefficient.

[0019] Furthermore, when the deviation adjustment term is a logarithmic deviation adjustment term, the calculation formula of the deviation adjustment term is as follows:

[0020]

[0021] Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, represents the expected value of the predicted current, and k represents the adjustment coefficient.

[0022] Furthermore, when the deviation adjustment item is a proportional deviation adjustment item, the calculation formula of the deviation adjustment item is as follows:

[0023]

[0024] Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, Represents the expected value of the predicted current.

[0025] Furthermore, when the deviation adjustment item is a deviation adjustment item based on a deep learning model, the calculation formula of the deviation adjustment item is as follows:

[0026] δ=f(t,c i ,s i ,σ);

[0027] Among them, δ represents the deviation adjustment term, s i represents the SOC value at the current moment, c i represents the current at the current moment, σ represents the statistical value of the static feature, t represents the time t, and f represents the deep learning model based on the time series prediction model.

[0028] The present invention also provides a dynamic prediction system for a power battery charging curve based on deviation adjustment, which is used to implement the dynamic prediction method for a power battery charging curve based on deviation adjustment as described in any one of the above items, and the system includes the following modules:

[0029] A data acquisition module, used to acquire historical charging data of the vehicle;

[0030] A data set construction module, connected to the data acquisition module, is used to determine the charging mode classification, the ideal charging curve corresponding to each classification, and the historical charging segment feature data of each classification according to the historical charging data of the vehicle, and to construct a data set according to the charging mode classification and the historical charging segment feature data of each classification;

[0031] A classification model building module is connected to the data set building module and is used to build a classification model and train the classification model using the data set to obtain a trained classification model;

[0032] The initial curve prediction module is connected to the classification model building module, and is used to obtain the real-time charging segment feature data, obtain the predicted charging mode classification through the trained classification model, and obtain the corresponding ideal charging curve as the initial prediction curve according to the predicted charging mode classification;

[0033] The deviation adjustment module is connected to the initial curve prediction module and is used to calculate the deviation adjustment item through the deviation adjustment formula according to the real-time charging segment characteristic data, and adjust the initial prediction curve based on the deviation adjustment item to obtain a corrected prediction curve.

[0034] The embodiments of the present invention have the following technical effects:

[0035] 1. Through accurate classification of charging modes and real-time deviation adjustment, the present invention can more accurately predict charging time, dynamically optimize the charging process, reduce charging time, and improve the utilization efficiency of charging piles and vehicle-mounted power batteries;

[0036] 2. The present invention provides four deviation adjustment methods, namely linear, logarithmic, proportional and deep learning (such as LSTM), for different computing power conditions. The linear, logarithmic and proportional adjustment methods have low computational complexity and are suitable for scenarios with limited computing power. When computing power is sufficient, the dynamic deviation adjustment method based on the deep learning model can further improve the prediction accuracy and real-time adjustment capability of the charging curve, and flexibly adapt to diverse charging environments.

[0037] 3. The present invention effectively solves the prediction error caused by the fluctuation of charging parameters such as current and voltage through a dynamic deviation adjustment module, ensuring that the prediction curve is highly matched with the actual charging behavior, especially under complex working conditions such as battery aging and ambient temperature changes, the present invention can still maintain high-precision prediction;

[0038] 4. Dynamic deviation adjustment can respond to abnormalities that may occur during charging, such as overcharging, undercharging or excessive current, in real time, and ensure the safety of battery use by correcting the charging curve in real time;

[0039] 5. For devices with lower computing power, the present invention provides linear, logarithmic and proportional deviation adjustment methods, which significantly reduce the requirements for hardware performance and expand the scope of application of the algorithm, so that it can also run stably in ordinary charging equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 is a flow chart of a method for dynamic prediction of a power battery charging curve based on deviation adjustment provided in an embodiment of the present invention;

[0042] Figure 2 It is a comparison diagram of an initial prediction curve and an actual charging process provided by an embodiment of the present invention;

[0043] Figure 3 is a schematic diagram of a current fluctuation that occurs during an actual charging process provided by an embodiment of the present invention;

[0044] Figure 4 It is a comparison diagram of a prediction curve corrected by a deviation adjustment formula and an actual charging process provided by an embodiment of the present invention;

[0045] Figure 5 It is a comparison diagram of an actual charging process, an initial prediction curve, and a prediction curve corrected by a deviation adjustment formula provided by an embodiment of the present invention;

[0046] Figure 6 It is a structural schematic diagram of a power battery charging curve dynamic prediction system based on deviation adjustment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0048] The charging behavior of new energy vehicles during charging is affected by multiple factors, including charging current, voltage, SOC (state of charge), ambient temperature, etc. These factors are closely related to the charging mode (such as constant current mode and constant voltage mode), and the changes in the charging curves under different modes have significant regularity.

[0049] In actual charging applications, dynamic changes such as battery SOC, current fluctuations, and ambient temperature have a significant impact on the accuracy of charging time prediction and the safety of the charging process. However, existing research has mostly focused on the fixed mode optimization of specific charging facilities, while ignoring the adaptability of vehicles in dynamic charging scenarios.

[0050] In order to solve the above problems, the present invention proposes a dynamic prediction method of a power battery charging curve based on deviation adjustment. Figure 1 is a flow chart of a method for dynamic prediction of a power battery charging curve based on deviation adjustment provided by an embodiment of the present invention, see Figure 1 , specifically including:

[0051] S1. Obtain vehicle historical charging data.

[0052] In some embodiments, all complete charging segments are extracted from the historical charging data of the vehicle, including the historical charging data of each segment, including: time t, voltage V i 、Current c i , Temperature T i and SOC value i wait.

[0053] S2. Determine charging mode classification, ideal charging curves corresponding to each classification, and historical charging segment feature data of each classification according to historical charging data of the vehicle, and construct a data set according to the charging mode classification and the historical charging segment feature data of each classification.

[0054] In some embodiments, the vehicle's historical charging data is analyzed to summarize possible charging modes of the vehicle, such as constant current mode (CC) and constant voltage mode (CV). These modes form the basic characteristics of charging behavior and provide data support for the training of subsequent classification models.

[0055] In some embodiments, an initial prediction curve C under the current charging mode is generated according to the classification result. The prediction curve is based on a mathematical model of the classification mode and combines the relationship between the charging current, voltage and SOC to calculate the charging time and charging rate.

[0056] The characteristic data of historical charging segments are standardized, and the characteristic values ​​such as current and voltage are scaled to the interval [0,1] to eliminate the influence of characteristic dimensions on model training. The missing data are processed by the low-rank matrix filling method to ensure the consistency and completeness of the charging data. A data set is constructed according to the charging mode classification and the historical charging segment characteristic data of each classification.

[0057] S3. Build a classification model and use the data set to train the classification model to obtain a trained classification model.

[0058] In some embodiments, the classification model is an LSTM model, the input of the classification model is the historical charging segment feature data, and the output of the classification model is the charging mode classification corresponding to the historical charging segment feature data. The LSTM model is trained using a data set, with the input being the time series features (time, current, etc.), and the output being the predicted charging mode category. The LSTM model parameters are optimized through annotations in the historical data to improve the classification accuracy.

[0059] S4. Acquire real-time charging segment feature data, obtain a predicted charging mode classification through a trained classification model, and obtain a corresponding ideal charging curve as an initial prediction curve based on the predicted charging mode classification.

[0060] In some embodiments, real-time charging segment feature data is obtained and input into a trained classification model. The model output result is the charging mode to which the current real-time charging segment feature data belongs, such as constant current mode or constant voltage mode. According to the predicted charging mode classification, the corresponding ideal charging curve is obtained as the initial prediction curve.

[0061] Figure 2 is a comparison diagram of an initial prediction curve and an actual charging process provided by an embodiment of the present invention, Figure 3 is a schematic diagram of current fluctuations that occur during an actual charging process provided by an embodiment of the present invention, see Figure 2 and Figure 3 , it can be seen that the initial prediction curve is consistent with the actual charging curve in terms of overall trend, but in response to emergencies (such as Figure 3 The current phase fluctuations shown are not adequate.

[0062] S5. Calculate a deviation adjustment item through a deviation adjustment formula according to the real-time charging segment characteristic data, and adjust the initial prediction curve based on the deviation adjustment item to obtain a corrected prediction curve.

[0063] In some embodiments, the deviation adjustment term is calculated according to the deviation adjustment formula, and the initial prediction curve is adjusted based on the deviation adjustment term to obtain a corrected prediction curve, wherein the deviation adjustment formula is as follows:

[0064] s i+1=s i +ωc i Δt+δ=f(t,c i Δt,s i ,σ);

[0065] Among them, s i Indicates the SOC value at the current moment, s i+1 represents the SOC value at the next moment, c i represents the current at the current moment, Δt represents the sampling time interval, δ represents the deviation adjustment term, σ represents the statistical value of static characteristics (such as ambient temperature, battery aging, etc.), ω represents the coefficient of the linear relationship between current and SOC value, f(t,c i Δt,s i ,σ) represents the coefficient of the linear relationship between current and SOC value and the relationship between current and static characteristics, and t represents time t.

[0066] In some embodiments, the bias adjustment item is divided into a linear bias adjustment item, a logarithmic bias adjustment item, a proportional bias adjustment item, and a bias adjustment item based on a deep learning model.

[0067] In some embodiments, when the deviation adjustment term is a linear deviation adjustment term, the calculation formula of the deviation adjustment term is as follows:

[0068]

[0069] Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, It represents the expected value of the predicted current, k represents the adjustment coefficient, and in the formula of the linear deviation adjustment term, k is the linear proportional coefficient.

[0070] The linear deviation adjustment item is characterized by simple calculation, suitable for scenarios with extremely low computing power, and can quickly implement deviation adjustment.

[0071] In some embodiments, when the deviation adjustment term is a logarithmic deviation adjustment term, the calculation formula of the deviation adjustment term is as follows:

[0072]

[0073] Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, It represents the expected value of the predicted current, and k represents the adjustment coefficient. In the formula of the logarithmic deviation adjustment term, k is the offset, which is used to adjust the input range of the logarithmic function to ensure stable calculation.

[0074] The logarithmic deviation adjustment item enhances the ability to adjust larger deviations by taking the logarithm of the current difference. Compared with the linear method, it is more suitable for scenarios with large deviations and requiring nonlinear adjustment.

[0075] In some embodiments, when the deviation adjustment item is a proportional deviation adjustment item, the calculation formula of the deviation adjustment item is as follows:

[0076]

[0077] Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, Represents the expected value of the predicted current.

[0078] The proportional deviation adjustment item adjusts the current value at the next moment according to the proportional relationship between the current current and the predicted value. It is suitable for scenarios where the charging curve shows a proportional change pattern, and the calculation complexity is lower than that of the deep learning method.

[0079] In some embodiments, when the deviation adjustment item is a deviation adjustment item based on a deep learning model, the calculation formula of the deviation adjustment item is as follows:

[0080] δ=f(t,c i ,s i ,σ);

[0081] Among them, δ represents the deviation adjustment term, s i represents the SOC value at the current moment, c i represents the current at the current moment, σ represents the statistical value of the static feature, t represents the time t, and f represents the deep learning model based on the time series prediction model.

[0082] The deep bias adjustment item can capture complex nonlinear characteristics and multivariate relationships, and is suitable for scenarios with sufficient computing power and high requirements for prediction accuracy.

[0083] In the scenario of insufficient computing power, the above linear, logarithmic and proportional deviation adjustment methods have the advantages of simple calculation and low resource consumption, and are efficient solutions.

[0084] When computing power allows and higher precision is required, using a deep learning deviation adjustment method based on LSTM can significantly improve the deviation correction capability and curve optimization effect, and is a more preferred solution.

[0085] Through the combination of these four deviation adjustment methods, the present invention can flexibly adapt to the needs of different computing power and charging scenarios, which not only ensures the computing efficiency but also improves the matching degree between the charging curve and the actual charging behavior.

[0086] In some embodiments, the initial prediction curve is adjusted based on the deviation adjustment item, and the adjustment can be performed by directly superimposing the mapping method. The real-time corrected charging prediction curve C adjusted =C+δ, the corrected prediction curve is more consistent with the current charging state, significantly improving the prediction accuracy and charging efficiency. Other mapping methods can also be adopted according to actual conditions, which are not limited here.

[0087] Figure 4 This is a comparison diagram of a prediction curve corrected by a deviation adjustment formula and an actual charging process provided by an embodiment of the present invention, see Figure 4 After adjusting the prediction curve by using the deep learning adjustment item in the deviation adjustment item, the corrected prediction curve is more in line with the actual charging process and accurately predicts the duration required for this battery charging stage.

[0088] Figure 5 This is a comparison diagram of an actual charging process, an initial prediction curve, and a prediction curve corrected by a deviation adjustment formula provided by an embodiment of the present invention, see Figure 5 It can be clearly seen that the dynamic prediction method of power battery charging curve based on deviation adjustment proposed by this method can effectively solve the problem of inaccurate curve fitting in the dynamic charging process, especially in overcharging prediction.

[0089] The present invention combines charging mode classification with dynamic deviation adjustment to achieve accurate prediction of charging time in multiple scenarios. Provides a variety of deviation adjustment methods, taking into account application scenarios with limited computing power and high precision requirements. The optimized charging curve can reduce battery loss, extend battery life, and thus improve the safety of the charging process. In summary, from the perspective of new energy vehicles, the present invention achieves intelligent optimization of charging behavior through classification and adjustment, and has significant practical application value.

[0090] The present invention also proposes a dynamic prediction system for a power battery charging curve based on deviation adjustment, which is used to implement the above-mentioned dynamic prediction method for a power battery charging curve based on deviation adjustment. Figure 6 is a schematic diagram of a dynamic prediction system for a power battery charging curve based on deviation adjustment provided by an embodiment of the present invention, see Figure 6 , the system includes the following modules:

[0091] A data acquisition module, used to acquire historical charging data of the vehicle;

[0092] A data set construction module, connected to the data acquisition module, is used to determine the charging mode classification, the ideal charging curve corresponding to each classification, and the historical charging segment feature data of each classification according to the historical charging data of the vehicle, and to construct a data set according to the charging mode classification and the historical charging segment feature data of each classification;

[0093] A classification model building module is connected to the data set building module and is used to build a classification model and train the classification model using the data set to obtain a trained classification model;

[0094] The initial curve prediction module is connected to the classification model building module, and is used to obtain the real-time charging segment feature data, obtain the predicted charging mode classification through the trained classification model, and obtain the corresponding ideal charging curve as the initial prediction curve according to the predicted charging mode classification;

[0095] The deviation adjustment module is connected to the initial curve prediction module and is used to calculate the deviation adjustment item through the deviation adjustment formula according to the real-time charging segment characteristic data, and adjust the initial prediction curve based on the deviation adjustment item to obtain a corrected prediction curve.

[0096] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0097] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic prediction method for power battery charging curve based on deviation adjustment, characterized in that: The steps include: S1. Obtaining historical charging data of the vehicle; wherein the historical charging data includes time, voltage, current, temperature and SOC value; S2. Determine the charging mode classification, the ideal charging curve corresponding to each classification, and the historical charging segment feature data of each classification according to the historical charging data of the vehicle, and construct a data set according to the charging mode classification and the historical charging segment feature data of each classification; S3, constructing a classification model, and using the data set to train the classification model to obtain a trained classification model; S4, acquiring real-time charging segment feature data, obtaining a predicted charging mode classification through the trained classification model, and obtaining a corresponding ideal charging curve as an initial prediction curve according to the predicted charging mode classification; S5. Calculate a deviation adjustment item through a deviation adjustment formula according to the real-time charging segment characteristic data, and adjust the initial prediction curve based on the deviation adjustment item to obtain a corrected prediction curve.

2. The method for dynamic prediction of power battery charging curve based on deviation adjustment according to claim 1 is characterized in that: In S3, the classification model is an LSTM model, the input of the classification model is historical charging segment feature data, and the output of the classification model is the charging mode classification corresponding to the historical charging segment feature data.

3. The method for dynamic prediction of power battery charging curve based on deviation adjustment according to claim 1, characterized in that: In S5, the deviation adjustment item is calculated by the deviation adjustment formula according to the real-time charging segment characteristic data, and the initial prediction curve is adjusted based on the deviation adjustment item to obtain a corrected prediction curve, wherein the deviation adjustment formula is as follows: s i+1 =s i +ωc i Δt+δ=f(t,c i Δt,s i ,σ); Among them, s i Indicates the SOC value at the current moment, s i+1 represents the SOC value at the next moment, c i represents the current at the current moment, Δt represents the sampling time interval, δ represents the deviation adjustment term, σ represents the statistical value of the static characteristic, ω represents the coefficient of the linear relationship between the current and the SOC value, and f(t,c i Δt,s i ,σ) represents the coefficient of the linear relationship between current and SOC value and the relationship between current and static characteristics, and t represents time t; among them, the deviation adjustment item is divided into linear deviation adjustment item, logarithmic deviation adjustment item, proportional deviation adjustment item and deviation adjustment item based on deep learning model.

4. The method for dynamic prediction of power battery charging curve based on deviation adjustment according to claim 3 is characterized in that: When the deviation adjustment item is a linear deviation adjustment item, the calculation formula of the deviation adjustment item is as follows: Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, represents the expected value of the predicted current, and k represents the adjustment coefficient.

5. The method for dynamic prediction of power battery charging curve based on deviation adjustment according to claim 3 is characterized in that: When the deviation adjustment item is a logarithmic deviation adjustment item, the calculation formula of the deviation adjustment item is as follows: Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, represents the expected value of the predicted current, and k represents the adjustment coefficient.

6. The method for dynamic prediction of power battery charging curve based on deviation adjustment according to claim 3 is characterized in that: When the deviation adjustment item is a proportional deviation adjustment item, the calculation formula of the deviation adjustment item is as follows: Among them, δ represents the deviation adjustment term, c i Indicates the charging current at the current moment, Represents the expected value of the predicted current.

7. The method for dynamic prediction of power battery charging curve based on deviation adjustment according to claim 3 is characterized in that: When the deviation adjustment item is a deviation adjustment item based on a deep learning model, the calculation formula of the deviation adjustment item is as follows: δ=f(t,c i ,s i ,s); Among them, δ represents the deviation adjustment term, s i represents the SOC value at the current moment, c i represents the current at the current moment, σ represents the statistical value of the static feature, t represents the time t, and f represents the deep learning model based on the time series prediction model.

8. A system for dynamic prediction of a power battery charging curve based on deviation adjustment, used to implement a method for dynamic prediction of a power battery charging curve based on deviation adjustment as described in any one of claims 1 to 7 above, characterized in that: The system includes the following modules: A data acquisition module, used to acquire historical charging data of the vehicle; A data set construction module, connected to the data acquisition module, for determining the charging mode classification, the ideal charging curve corresponding to each classification, and the historical charging segment characteristic data of each classification according to the historical charging data of the vehicle, and constructing a data set according to the charging mode classification and the historical charging segment characteristic data of each classification; A classification model building module, connected to the data set building module, is used to build a classification model, and train the classification model using the data set to obtain a trained classification model; An initial curve prediction module, connected to the classification model building module, is used to obtain real-time charging segment feature data, obtain a predicted charging mode classification through the trained classification model, and obtain a corresponding ideal charging curve as an initial prediction curve according to the predicted charging mode classification; The deviation adjustment module is connected to the initial curve prediction module and is used to calculate the deviation adjustment item through the deviation adjustment formula according to the real-time charging segment characteristic data, and adjust the initial prediction curve based on the deviation adjustment item to obtain a corrected prediction curve.

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