Monitoring system and method for cooperation state between vehicle and charging stand table in electric vehicle charging / discharging process

Abstract

To provide, in particular, monitoring system and method for cooperation state between a vehicle and a charging stand table in electric vehicle charging / discharging process in a technical field for electrical parameter measurement.SOLUTION: A monitoring system includes: a vehicle battery monitoring module that collects vehicle battery state parameters in real time; a charging stand table monitoring module that monitors charging stand table state parameters in real time; a monitoring module that processes the vehicle battery state parameters and the charging stand table state parameters, and determines vehicle battery charging / discharging state on the basis of a preset formula; and a processing module that predicts life of a vehicle module on the basis of the vehicle battery charging / discharging state, the vehicle battery state parameters, and the charging stand table state parameters. A battery life is rationally predicted through parameters of a cooperation state between a vehicle and the charging stand table, a plurality of influence parameters are combined thereby correcting the prediction of the battery life, and the battery life is accurately and effectively evaluated.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to the technical field of electrical variable measurement, and particularly to a monitoring system and method for the cooperation state between a vehicle and a charging stand in the charging and discharging process of an electric vehicle.

Background Art

[0002] With the improvement of battery performance, electric vehicles have been widely popularized. It is considered that the mainstream of future vehicle development is that the number of electric vehicles will increase year by year, and some internal combustion engine vehicles will tend to be replaced. At the same time, as the technology of electric vehicles becomes more and more mature, the management and service of electric vehicles are also more standardized. The standards of electric vehicles have also emerged as the latest series and standardized models, and electric vehicles will bring changes to the automotive era.

[0003] However, in the existing technology, it is difficult to evaluate the health state of the battery in the charging and discharging process of an electric vehicle. The main influencing factors include the non-linear change of battery parameters, the change of battery performance with the passage of time and the use environment, which makes it difficult to construct an accurate mathematical model. In addition, due to the randomness and unpredictability of the battery degradation process, there is no clear degradation rule at present. Furthermore, there is also the difficulty of how to effectively integrate various monitoring data to conduct a reasonable analysis for evaluating the health state of the battery. Therefore, the monitoring system and method for the cooperation state between a vehicle and a charging stand in the charging and discharging process of an electric vehicle are urgent technical problems that those skilled in the art need to solve.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention aims to provide a monitoring system and method for the cooperation state between a vehicle and a charging stand in the electric vehicle charging and discharging process. The present invention reasonably predicts the battery life through the parameters of the cooperation state between the vehicle and the charging stand, corrects the battery life prediction by combining multiple influencing parameters, and accurately and effectively evaluates the battery life.

Means for Solving the Problems

[0005] To achieve the above object, the present invention provides the following technical solutions. A system for monitoring the cooperation state between a vehicle and a charging stand in the electric vehicle charging and discharging process, A vehicle battery monitoring module used to collect vehicle battery state parameters in real time, where the state parameters of the vehicle battery include battery terminal voltage Vd, battery charging and discharging current Id, and battery temperature Td, A charging stand monitoring module used to monitor the state parameters of the charging stand in real time, where the state parameters of the charging stand include charging stand voltage Vc, charging stand current Ic, and charging stand power Pc, A data communication module used to transmit the state parameters of the vehicle battery and the state parameters of the charging stand through wireless communication technology, A monitoring module that receives the state parameters of the vehicle battery and the state parameters of the charging stand, processes the state parameters of the vehicle battery and the state parameters of the charging stand, and is used to determine the charging and discharging state of the vehicle battery based on a preset formula, Including a processing module used to predict the life of the vehicle battery based on the charging and discharging state of the vehicle battery, the state parameters of the vehicle battery, and the state parameters of the charging stand.

[0006] In some embodiments of the present application, the monitoring module determines the charging and discharging state of the vehicle battery based on a preset formula, which includes the following. Calculate the remaining amount of the vehicle battery based on the battery terminal voltage Vd and the battery charge and discharge current Id, and determine the charge and discharge state of the vehicle battery based on the remaining amount of the vehicle battery. The preset formula is as follows.

[0007]

Number

[0008] In the formula, SOC is the remaining amount of the vehicle battery, V bat is the terminal voltage of the vehicle battery, V min is the minimum allowable voltage of the vehicle battery, V max is the maximum allowable voltage of the vehicle battery, and When the remaining amount of the vehicle battery exceeds 50%, the vehicle battery is determined to be in a charging state. When the remaining amount of the vehicle battery is less than 50%, the vehicle battery is determined to be in a discharging state. When the remaining amount of the vehicle battery is 50%, the vehicle battery is determined to be in a fully charged state.

[0009] In some embodiments of the present application, when the monitoring module determines that the vehicle battery is in a charging state, it further generates a charging curve based on the state parameters of the charging stand, and is also used to obtain the charging speed vd of the vehicle battery based on the charging curve. The processing module is further used to predict the life of the vehicle battery based on the charging speed vd of the vehicle battery. A preset charging speed matrix T0 and a preset remaining life time matrix A are preset in the processing module. For the preset remaining life time matrix A, A(A1, A2, A3, A4) is set, where A1 is the first preset remaining life time, A2 is the second preset remaining life time, A3 is the third preset remaining life time, A4 is the fourth preset remaining life time, and A1 < A2 < A3 < A4 < 5 years. For the preset charging speed matrix T0, set T0 (T01, T02, T03, T04), where T01 is the first preset charging speed, T02 is the second preset charging speed, T03 is the third preset charging speed, T04 is the fourth preset charging speed, and T01 < T02 < T03 < T04. The processing module is used to select the corresponding remaining life time based on the relationship between vd and the preset charging speed matrix T0 to obtain the predicted life of the vehicle battery. When vd < T01, select the fourth preset remaining life time A4 as the predicted life of the vehicle battery. When T01 ≤ vd < T02, select the third preset remaining life time A3 as the predicted life of the vehicle battery. When T02 ≤ vd < T03, select the second preset remaining life time A2 as the predicted life of the vehicle battery. When T03 ≤ vd < T04, select the first preset remaining life time A1 as the predicted life of the vehicle battery.

[0010] In some embodiments of the present application, the processing module is also used to obtain the past charging times k of the vehicle battery. The processing module is also set with a preset past charging times matrix R0 and a preset remaining life time correction coefficient matrix B. For the preset remaining life time correction coefficient matrix B, set B (B1, B2, B3, B4), where B1 is the first preset remaining life time correction coefficient, B2 is the second preset remaining life time correction coefficient, B3 is the third preset remaining life time correction coefficient, B4 is the fourth preset remaining life time correction coefficient, and 0.6 < B1 < B2 < B3 < B4 < 1. For the preset past charge count matrix R0 above, set R0(R01, R02, R03, R04), where R01 is the first preset past charge count, R02 is the second preset past charge count, R03 is the third preset past charge count, R04 is the fourth preset past charge count, and R01 < R02 < R03 < R04. The above processing module is used to select the corresponding remaining life time correction coefficient based on the relationship between k and the preset past charge count matrix R0 and correct the remaining life time. When k < R01, the fourth preset remaining life time correction coefficient B4 is selected to correct the fourth preset remaining life time A4, and the corrected remaining life time is A4 × B4. When R01 ≤ k < R02, the third preset remaining life time correction coefficient B3 is selected to correct the third preset remaining life time A3, and the corrected remaining life time is A3 × B3. When R02 ≤ k < R03, the second preset remaining life time correction coefficient B2 is selected to correct the second preset remaining life time A2, and the corrected remaining life time is A2 × B2. When R03 ≤ k < R04, the first preset remaining life time correction coefficient B1 is selected to correct the first preset remaining life time A1, and the corrected remaining life time is A1 × B1.

[0011] In some embodiments of the present application, when it is determined that the vehicle battery is in a discharging state, the monitoring module further generates a discharge curve based on the state parameters of the vehicle battery and the remaining amount of the vehicle battery, and is used to obtain the discharge rate vt of the vehicle battery based on the discharge curve. The above processing module is further set with a preset discharge rate matrix W0 and a preset remaining life time quadratic correction coefficient matrix C. For the above preset remaining life time quadratic correction coefficient matrix C, C(C1, C2, C3, C4) is set. Here, C1 is the first preset remaining life time quadratic correction coefficient, C2 is the second preset remaining life time quadratic correction coefficient, C3 is the third preset remaining life time quadratic correction coefficient, C4 is the fourth preset remaining life time quadratic correction coefficient, and 0.6 < C1 < C2 < C3 < C4 < 1. For the above preset discharge rate matrix W0, W0(W01, W02, W03, W04) is set. Here, W01 is the first preset discharge rate, W02 is the second preset discharge rate, W03 is the third preset discharge rate, W04 is the fourth preset discharge rate, and W01 < W02 < W03 < W04. The above processing module is further used to select a corresponding remaining life time quadratic correction coefficient based on the relationship between vt and the above preset discharge rate matrix W0 and perform quadratic correction on each corrected preset remaining life time. When vt < W01, the fourth preset remaining life time quadratic correction coefficient C4 is selected to perform quadratic correction on the corrected preset fourth remaining life time A4, and the corrected remaining life time is A4 × B4 × C4. When W01 ≤ vt < W02, the third preset remaining life time quadratic correction coefficient C3 is selected to perform quadratic correction on the corrected preset third remaining life time A3, and the corrected remaining life time is A3 × B3 × C3. When W02 ≤ vt < W03, the second preset remaining life time quadratic correction coefficient C2 is selected to perform quadratic correction on the corrected preset second remaining life time A2, and the corrected remaining life time is A2 × B2 × C2. When W03 ≤ vt < W04, the first preset remaining life time quadratic correction coefficient C1 is selected to perform quadratic correction on the corrected preset first remaining life time A1, and the corrected remaining life time is A1 × B1 × C1.

[0012] In some embodiments of the present application, the above processing module is further used to perform a fault prediction on the vehicle battery based on the predicted life of the vehicle battery, where Performing a fault prediction on the vehicle battery by the above processing module includes acquiring the past state parameters of the vehicle battery, where the past state parameters of the vehicle battery include the past terminal voltage of the battery, the past charge and discharge current of the battery, and the past temperature of the battery, and arranging the acquired past state parameters of the vehicle battery based on a time series, and preprocessing the arranged past state parameters, and performing learning training on the data in the past state parameters based on SVR to construct a fault prediction model, and performing a fault prediction on the vehicle battery based on the fault prediction model and the state parameters of the vehicle battery, and performing a residual threshold test and a fault warning based on the fault prediction result of the vehicle battery by the fault prediction model.

[0013] In some embodiments of the present application, preprocessing the arranged past state parameters of the above processing module includes removing abnormal value data in the past state parameters, performing data interval processing on the data in the past state parameters, and performing a non-linear judgment on the data in the past state parameters to generate a sample format used to support vector machine learning training.

[0014] To achieve the above object, the present invention further provides a method for monitoring the cooperative state of a vehicle and a charging stand in an electric vehicle charging and discharging process, which is applied to a monitoring system for the cooperative state of a vehicle and a charging stand in an electric vehicle charging and discharging process accordingly. Collecting vehicle battery state parameters including the battery terminal voltage Vd, the battery charge and discharge current Id, and the battery temperature Td in real time, Monitoring the state parameters of the charging stand including the charging stand voltage Vc, the charging stand current Ic, and the charging stand power Pc in real time, Transmitting the state parameters of the vehicle battery and the state parameters of the charging stand through wireless communication technology, Receiving the state parameters of the vehicle battery and the state parameters of the charging stand, processing the state parameters of the vehicle battery and the state parameters of the charging stand, and determining the charge and discharge state of the vehicle battery based on a preset formula, Including predicting the life of the vehicle battery based on the charge and discharge state of the vehicle battery, the state parameters of the vehicle battery, and the state parameters of the charging stand.

[0015] In some embodiments of the present application, determining the charge and discharge state of the vehicle battery based on a preset formula includes Calculating the remaining amount of the vehicle battery based on the battery terminal voltage Vd and the battery charge and discharge current Id, and determining the charge and discharge state of the vehicle battery based on the remaining amount of the vehicle battery, The preset formula is as follows.

[0016]

Equation

[0017] In the formula, SOC is the remaining amount of the vehicle battery, V bat is the terminal voltage of the vehicle battery, V min is the minimum allowable voltage of the vehicle battery, V max is the maximum allowable voltage of the vehicle battery, and Here, when the remaining amount of the vehicle battery exceeds 50%, it is determined that the vehicle battery is in a charging state, When the remaining amount of the vehicle battery is less than 50%, the vehicle battery is determined to be in a discharged state. When the remaining amount of the vehicle battery is 50%, the vehicle battery is determined to be in a fully charged state.

[0018] In some embodiments of the present application, further, Based on the predicted life of the vehicle battery, a fault prediction is performed on the vehicle battery, where Performing a fault prediction on the vehicle battery includes acquiring the past state parameters of the vehicle battery, where the past state parameters of the vehicle battery include the past terminal voltage of the battery, the past charge and discharge current of the battery, and the past temperature of the battery, and arranging the acquired past state parameters of the vehicle battery based on time series, and preprocessing the arranged past state parameters, and performing learning training on the data in the past state parameters based on SVR to construct a fault prediction model, and performing a fault prediction on the vehicle battery based on the fault prediction model and the state parameters of the vehicle battery, and executing a residual threshold test and a fault warning based on the fault prediction result of the vehicle battery by the fault prediction model are included.

Advantages of the Invention

[0019] The present invention provides a monitoring system and method for the cooperative state of a vehicle and a charging stand in an electric vehicle charging and discharging process. Compared with the prior art, its beneficial effects are as follows. The present invention calculates the charging speed and the discharging speed through the state parameters of the vehicle battery and the state parameters of the charging stand, reasonably predicts the battery life, and combines the adaptive correction of related parameters to accurately evaluate the battery life. By combining with the machine learning method, a fault warning can be given according to the battery life.

Brief Description of the Drawings

[0020]

Figure 1

Figure 2

Embodiments for Carrying Out the Invention

[0021] Specific embodiments of the present invention will be described in more detail below with reference to the drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0022] In the description of the present application, the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is merely for explaining the present application and simplifying the explanation, and does not indicate or imply that the specified device or element must be configured and operated in a specific orientation or a specific orientation, and therefore cannot be construed as limiting the present application.

[0023] The terms "first" and "second" are used only for the purpose of description, and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the present application, unless otherwise specified, the meaning of "plurality" is two or more.

[0024] In the description of the present application, unless specifically described and limited otherwise, the terms "installation", "connection", and "attachment" should be understood in a broad sense. For example, they may be fixed connections, removable connections, or integral connections, mechanical or electrical connections, directly connected, indirectly connected through an intermediary, or even internal communication between two components. Those skilled in the art will be able to understand the specific meaning of the above terms in the present application on a case-by-case basis.

[0025] The long charging time is one of the concerns in promoting the popularization of new energy vehicles. As a solution, the first is to improve the charging efficiency, that is, to replenish up to 80% of the power in 30 minutes or 15 minutes, which is the so-called rapid charging technology. However, the requirements for the safety of the power battery are relatively high, and it also affects the battery life. The second is the battery swapping mode. Theoretically, it has the advantages of improving energy efficiency, improving the utilization efficiency of the power battery, controlling safety, and being beneficial to the commercialization of the power battery. However, there are also problems such as cost control, management systems, and standardization. Moreover, not all vehicles are suitable for the battery swapping mode.

[0026] Since the general charging method of electric vehicles is to adopt rapid charging technology, it is difficult to evaluate the health state of the battery in the charging and discharging process of electric vehicles. Due to the non-linear change of battery parameters, the change of battery performance with time and usage environment, it is difficult to construct an accurate mathematical model. Moreover, due to the randomness and unpredictability of the battery degradation process, there is no clear degradation rule at present. Furthermore, there is also the difficulty of how to effectively integrate various monitoring data to conduct a reasonable analysis for evaluating the health state of the battery.

[0027] Therefore, the present invention provides a monitoring system and method for the coordination state of a vehicle and a charging stand in an electric vehicle charging and discharging process, reasonably predicting the battery life through the parameters of the coordination state of the vehicle and the charging stand, correcting the prediction of the battery life by combining multiple influencing parameters, and accurately and effectively evaluating the battery life.

[0028] Referring to FIG. 1, an embodiment disclosed by the present invention is a monitoring system for the coordination state of a vehicle and a charging stand in an electric vehicle charging and discharging process, a vehicle battery monitoring module used to collect vehicle battery state parameters in real time, where the state parameters of the vehicle battery include the battery terminal voltage Vd, the battery charging and discharging current Id, and the battery temperature Td, and a charging stand monitoring module used to monitor the state parameters of the charging stand in real time, where the state parameters of the charging stand include the charging stand voltage Vc, the charging stand current Ic, and the charging stand power Pc, and a data communication module used to transmit the state parameters of the vehicle battery and the state parameters of the charging stand through wireless communication technology, and a monitoring module used to receive the state parameters of the vehicle battery and the state parameters of the charging stand, process the state parameters of the vehicle battery and the state parameters of the charging stand, and determine the charging and discharging state of the vehicle battery based on a preset formula, and including a processing module used to predict the life of the vehicle battery based on the charging and discharging state of the vehicle battery, the state parameters of the vehicle battery, and the state parameters of the charging stand.

[0029] In one specific embodiment of the present application, the monitoring module determines the charging and discharging state of the vehicle battery based on a preset formula, which calculates the remaining amount of the vehicle battery based on the battery terminal voltage Vd and the battery charging and discharging current Id, and determines the charging and discharging state of the vehicle battery based on the remaining amount of the vehicle battery, and the preset formula is as follows.

[0030]

Number

[0031] In the formula, SOC is the remaining amount of the vehicle battery, V bat is the terminal voltage of the vehicle battery, V min is the minimum allowable voltage of the vehicle battery, V max is the maximum allowable voltage of the vehicle battery, and In the formula, when the remaining amount of the vehicle battery exceeds 50%, the vehicle battery is determined to be in a charged state, when the remaining amount of the vehicle battery is less than 50%, the vehicle battery is determined to be in a discharged state, when the remaining amount of the vehicle battery is 50%, the vehicle battery is determined to be in a fully charged state.

[0032] Note that the above technical solution is applicable to lithium batteries, and the acquisition of the parameters therein is obtained by combining with a battery management system (BMS) or directly reading the electrical parameter sensor of the battery in actual application, whereby parameters such as the terminal voltage and charge and discharge current of the battery are obtained.

[0033] In one specific embodiment of the present application, when it is determined that the vehicle battery is in a charged state, the monitoring module is further used to generate a charging curve based on the state parameters of the charging stand and obtain the charging speed vd of the vehicle battery based on the charging curve. The processing module is further used to predict the life of the vehicle battery based on the charging speed vd of the vehicle battery. In the processing module, a preset charging speed matrix T0 and a preset remaining life time matrix A are preset. For the preset remaining life time matrix A, A(A1, A2, A3, A4) is set. Here, A1 is the first preset remaining life time, A2 is the second preset remaining life time, A3 is the third preset remaining life time, A4 is the fourth preset remaining life time, and A1 < A2 < A3 < A4 < 5 years. For the preset charging speed matrix T0, T0(T01, T02, T03, T04) is set. Here, T01 is the first preset charging speed, T02 is the second preset charging speed, T03 is the third preset charging speed, T04 is the fourth preset charging speed, and T01 < T02 < T03 < T04. The processing module is used to select the corresponding remaining life time based on the relationship between vd and the preset charging speed matrix T0 to be the predicted life of the vehicle battery. When vd < T01, the fourth preset remaining life time A4 is selected as the predicted life of the vehicle battery. When T01 ≤ vd < T02, the third preset remaining life time A3 is selected as the predicted life of the vehicle battery. When T02 ≤ vd < T03, the second preset remaining life time A2 is selected as the predicted life of the vehicle battery. When T03 ≤ vd < T04, the first preset remaining life time A1 is selected as the predicted life of the vehicle battery.

[0034] It is understandable that the faster the charging speed, the shorter the battery life. This is because rapid charging causes the balance of chemical reactions inside the battery to break down, leading to problems such as warping or deformation of the battery plates and an increase in the concentration of the acid solution. Therefore, the present invention can predict the life of the vehicle battery in combination with the charging speed and effectively evaluate the performance and reliability of the battery during use. Through prediction, the change situation of the battery life at different charging speeds can be confirmed, so that reasonable charging advice can be provided to users, the battery life can be extended, and the cruising range and performance of electric vehicles can be improved. Also, this is beneficial to battery manufacturers and electric vehicle manufacturers and helps to promote the development of the electric vehicle industry.

[0035] In one specific embodiment of the present application, the processing module is also used to obtain the past charging times k of the vehicle battery. The processing module is further set with a preset past charging times matrix R0 and a preset remaining life time correction coefficient matrix B. For the preset remaining life time correction coefficient matrix B, B(B1, B2, B3, B4) is set. Here, B1 is the first preset remaining life time correction coefficient, B2 is the second preset remaining life time correction coefficient, B3 is the third preset remaining life time correction coefficient, B4 is the fourth preset remaining life time correction coefficient, and 0.6 < B1 < B2 < B3 < B4 < 1. For the preset past charging times matrix R0, R0(R01, R02, R03, R04) is set. Here, R01 is the first preset past charging times, R02 is the second preset past charging times, R03 is the third preset past charging times, R04 is the fourth preset past charging times, and R01 < R02 < R03 < R04. The processing module is used to select the corresponding remaining life time correction coefficient based on the relationship between k and the preset past charging times matrix R0 to correct the remaining life time. When k < R01, the fourth preset remaining life time correction coefficient B4 is selected to correct the fourth preset remaining life time A4, and the corrected remaining life time is A4 × B4. When R01 ≤ k < R02, the third preset remaining life time correction coefficient B3 is selected to correct the third preset remaining life time A3, and the corrected remaining life time is A3 × B3. When R02 ≤ k < R03, the second preset remaining life time correction coefficient B2 is selected to correct the second preset remaining life time A2, and the corrected remaining life time is A2 × B2. When R03 ≤ k < R04, the first preset remaining life time correction coefficient B1 is selected to correct the first preset remaining life time A1, and the corrected remaining life time is A1 × B1.

[0036] It can be understood that the life of a lithium-ion battery is usually 3 to 5 years under normal usage conditions, but this is also affected by the number of charge cycles. As the number of charge cycles increases, the life becomes shorter. The present invention further improves the accuracy of the prediction result by correcting the life prediction of the vehicle battery in combination with the number of charge cycles.

[0037] In one specific embodiment of the present application, when it is determined that the vehicle battery is in a discharged state, the monitoring module further generates a discharge curve based on the state parameters of the vehicle battery and the remaining amount of the vehicle battery, and is used to obtain the discharge rate vt of the vehicle battery based on the discharge curve. A preset discharge rate matrix W0 and a preset remaining life time secondary correction coefficient matrix C are further set in the processing module. For the preset remaining life time secondary correction coefficient matrix C, C(C1, C2, C3, C4) is set, where C1 is the first preset remaining life time secondary correction coefficient, C2 is the second preset remaining life time secondary correction coefficient, C3 is the third preset remaining life time secondary correction coefficient, C4 is the fourth preset remaining life time secondary correction coefficient, and 0.6 < C1 < C2 < C3 < C4 < 1. For the preset discharge rate matrix W0, set W0 (W01, W02, W03, W04), where W01 is the first preset discharge rate, W02 is the second preset discharge rate, W03 is the third preset discharge rate, W04 is the fourth preset discharge rate, and W01 < W02 < W03 < W04. The processing module is further used to select the corresponding remaining life time secondary correction coefficient based on the relationship between vt and the preset discharge rate matrix W0 and perform secondary correction on each corrected preset remaining life time. When vt < W01, select the fourth preset remaining life time secondary correction coefficient C4 and perform secondary correction on the corrected preset fourth remaining life time A4. The corrected remaining life time is A4 × B4 × C4. When W01 ≤ vt < W02, select the third preset remaining life time secondary correction coefficient C3 and perform secondary correction on the corrected preset third remaining life time A3. The corrected remaining life time is A3 × B3 × C3. When W02 ≤ vt < W03, select the second preset remaining life time secondary correction coefficient C2 and perform secondary correction on the corrected preset second remaining life time A2. The corrected remaining life time is A2 × B2 × C2. When W03 ≤ vt < W04, select the first preset remaining life time secondary correction coefficient C1 and perform secondary correction on the corrected preset first remaining life time A1. The corrected remaining life time is A1 × B1 × C1.

[0038] It is understandable that the discharge rate has a great impact on the battery life of the vehicle. The faster the discharge rate, the shorter the battery life. This is because rapid discharge disrupts the balance of chemical reactions within the battery and accelerates the deterioration of battery performance. Conversely, the slower the discharge rate of the battery, the longer its life. The present invention can predict the battery life through the discharge rate and confirm the changes in battery life at different discharge rates, thereby providing a basis for battery management and maintenance, optimizing the battery usage conditions, extending the battery life, improving the battery performance, enhancing the overall performance of the vehicle, and helping to detect potential battery problems in advance for timely repair and replacement.

[0039] In one specific embodiment of the present application, the processing module is further used to perform a fault prediction on the vehicle battery based on the predicted life of the vehicle battery. Here, Performing a fault prediction on the vehicle battery by the processing module includes acquiring the past state parameters of the vehicle battery, where the past state parameters of the vehicle battery include the past terminal voltage of the battery, the past charge and discharge current of the battery, and the past temperature of the battery, and arranging the acquired past state parameters of the vehicle battery based on time series and preprocessing the arranged past state parameters, and performing learning training on the data in the past state parameters based on SVR to construct a fault prediction model, and performing a fault prediction on the vehicle battery based on the fault prediction model and the state parameters of the vehicle battery, and executing a residual threshold test and issuing a fault warning based on the fault prediction result of the vehicle battery by the fault prediction model.

[0040] In one specific embodiment of the present application, for the vehicle battery, performing a fault prediction and constructing a fault prediction model includes Perform regression analysis using SVM, that is, a support vector regression machine (SVR). Its implementation is to map the data X from the input space to a high-dimensional feature space G through a non-linear mapping φ and perform linear regression in this space. Considering the training set T = {(x1, y1),..., (x l , y l )} ∈ (R n × γ) l , where x i ∈ R n , y i ∈ γ = R, i = 1,..., l. To simplify the regression problem, a loss function with low sensitivity is introduced. This algorithm, also called ε-SVR, is the standard algorithm for support vector machine regression. The following equation is used to estimate the function.

[0041]

Equation

[0042] In the equation, b is the bias amount.

[0043] When optimizing the estimation function, take the extreme value of the optimization target.

[0044]

Equation

[0045] Then it becomes as follows.

[0046]

Equation

[0047] In the equation, C is the penalty coefficient used to achieve a compromise point between the empirical risk and the reliability risk. The larger C is, the higher the ability to fit the data. ξ i and ξ * iis the relaxation coefficient and is used to control the linearly inseparable boundary. ε is used to control the regression approximation error and the generalization ability of the model and is defined as follows.

[0048]

Number

[0049] By introducing the Lagrangian function and using the dual form of the optimization problem, the following regression function is finally obtained.

[0050]

Number

[0051] In the formula, α, α * i are ≥ 0, and γ, γ * i = ≥ 0, i = 1,..., l. According to the KKT theorem, the following can be obtained.

[0052]

Number

[0053] Therefore, in the case of non - linear support vector machine regression, the regression function is expressed as follows.

[0054]

Number

[0055] The kernel function selects the radial basis kernel function (RBF).

[0056]

Number

[0057] It is understandable that the present invention grasps the state of the battery in real time in combination with the method of machine learning, constructs a fault prediction model based on SVR, predicts the health state based on the current data and past data, and predicts possible faults.

[0058] In one specific embodiment of the present application, preprocessing the past state parameters arranged by the processing module includes removing abnormal value data in the past state parameters, intervalizing the data in the past state parameters, performing non-linear judgment on the data in the past state parameters, and generating a sample format used to support vector machine learning training.

[0059] Based on the same technical concept, as shown in FIG. 2, the present invention further provides a monitoring method for the cooperative state of a vehicle and a charging stand in an electric vehicle charging and discharging process, which is applied to a monitoring system for the cooperative state of a vehicle and a charging stand in an electric vehicle charging and discharging process, collecting in real time vehicle battery state parameters including battery terminal voltage Vd, battery charge and discharge current Id, and battery temperature Td, monitoring in real time the state parameters of the charging stand including charging stand voltage Vc, charging stand current Ic, and charging stand power Pc, transmitting the state parameters of the vehicle battery and the state parameters of the charging stand through wireless communication technology, receiving the state parameters of the vehicle battery and the state parameters of the charging stand, processing the state parameters of the vehicle battery and the state parameters of the charging stand, and judging the charge and discharge state of the vehicle battery based on a preset formula, including predicting the life of the vehicle battery based on the charge and discharge state of the vehicle battery, the state parameters of the vehicle battery, and the state parameters of the charging stand.

[0060] In one specific embodiment of the present application, determining the charge and discharge state of the vehicle battery based on the preset formula includes the following. Calculating the remaining amount of the vehicle battery based on the battery terminal voltage Vd and the battery charge and discharge current Id, and determining the charge and discharge state of the vehicle battery based on the remaining amount of the vehicle battery. The preset formula is as follows.

[0061]

Number

[0062] In the formula, SOC is the remaining amount of the vehicle battery, V bat is the terminal voltage of the vehicle battery, V min is the minimum allowable voltage of the vehicle battery, V max is the maximum allowable voltage of the vehicle battery, and When the remaining amount of the vehicle battery exceeds 50%, the vehicle battery is determined to be in a charged state. When the remaining amount of the vehicle battery is less than 50%, the vehicle battery is determined to be in a discharged state. When the remaining amount of the vehicle battery is 50%, the vehicle battery is determined to be in a fully charged state.

[0063] It should be noted that the above technical solution is applicable to lithium batteries, and the acquisition of the parameters therein is obtained by combining with a battery management system (BMS) or directly reading the electrical parameter sensors of the battery in actual application, whereby parameters such as the terminal voltage and charge and discharge current of the battery are obtained.

[0064] In one specific embodiment of the present application, it further includes the following. Performing a fault prediction on the vehicle battery based on the predicted life of the vehicle battery, where Performing a fault prediction on the vehicle battery includes Obtaining the past state parameters of the vehicle battery including the past terminal voltage of the battery, the past charge and discharge current of the battery, and the past temperature of the battery, and Arrange the acquired past state parameters of the vehicle battery based on the time series, and preprocess the arranged past state parameters. Execute learning training on the data in the past state parameters based on SVR, and construct a failure prediction model. Predict failures for the vehicle battery based on the failure prediction model and the state parameters of the vehicle battery. Based on the vehicle battery failure prediction result by the failure prediction model, execute a residual threshold test and issue a failure warning.

[0065] In summary, the present invention calculates the charging speed and the discharging speed through the state parameters of the vehicle battery and the state parameters of the charging stand, reasonably predicts the battery life, and combines the adaptive correction of related parameters to accurately evaluate the battery life. By combining with the machine learning method, a failure warning can be issued according to the battery life. The present invention has the advantages of being accurate and intelligent at the same time.

[0066] The above is only one embodiment of the present invention and does not limit the scope of the present invention. Any structural changes made based on the present invention, all structural changes made based on the present invention, are considered to be within the protection scope of the present invention and are subject to limitations as long as the essence of the present invention is not lost.

[0067] Those skilled in the art can clearly understand that for the sake of convenience and simplification of the description, for the specific working processes of the above system and related descriptions, reference can be made to the corresponding processes in the embodiments of the above method, and will not be described again here.

[0068] Note that the system provided by the above embodiments is only exemplified by taking the division of the above function modules as an example. In actual applications, the above functions can be assigned to different function modules as needed. That is, the modules or steps in the embodiments of the present invention can be further decomposed or recombined. For example, the modules in the above embodiments can be combined into one module, or further divided into a plurality of sub-modules to perform all or part of the above functions.

[0069] A person skilled in the art can realize each example of the modules and method steps described in connection with the embodiments disclosed in this specification by electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be arranged in a random access memory (RAM), memory, read-only memory (ROM), electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or other forms of storage media known in the technical field. To clearly show the compatibility between electronic hardware and software, the configurations and steps of each embodiment are generally described from a functional perspective in the above description. Whether these functions are executed as electronic hardware or as software depends on the specific application of the technical solution and design constraints. A person skilled in the art can use different methods to implement the functions described for each specific application, but such implementations should not be considered to exceed the scope of the present invention.

[0070] The term "comprising" or other similar terms mean that a process, method, article, or apparatus / device containing a list of elements includes not only those elements but also other elements not explicitly listed or inherent to the process, method, article, or apparatus / device.

[0071] The above technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings. However, it will be readily understood by those skilled in the art that the protection scope of the present invention is not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions are included in the protection scope of the present invention. The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of the present invention.

Claims

1. A vehicle battery monitoring module used to collect vehicle battery state parameters in real time, where the state parameters of the vehicle battery include battery terminal voltage Vd, battery charge and discharge current Id, and battery temperature Td, and A charging stand monitoring module used to monitor the state parameters of the charging stand in real time, where the state parameters of the charging stand include charging stand voltage Vc, charging stand current Ic, and charging stand power Pc, and A data communication module used to transmit the state parameters of the vehicle battery and the state parameters of the charging stand through wireless communication technology, and A monitoring module used to receive the state parameters of the vehicle battery and the state parameters of the charging stand, process the state parameters of the vehicle battery and the state parameters of the charging stand, and determine the charge and discharge state of the vehicle battery based on a preset formula, and Including a processing module used to predict the life of the vehicle battery based on the charge and discharge state of the vehicle battery, the state parameters of the vehicle battery, and the state parameters of the charging stand, wherein the monitoring module is further used to generate a charging curve based on the state parameters of the charging stand when it is determined that the vehicle battery is in a charged state, and also used to obtain the charging speed vd of the vehicle battery based on the charging curve, the processing module is further used to predict the life of the vehicle battery based on the charging speed vd of the vehicle battery, a preset charging speed matrix T0 and a preset remaining life time matrix A are preset in the processing module, for the preset remaining life time matrix A, A(A1, A2, A3, A4) is set, where A1 is the first preset remaining life time, A2 is the second preset remaining life time, A3 is the third preset remaining life time, A4 is the fourth preset remaining life time, and A1 < A2 < A3 < A4 < 5 years, Set T0 (T01, T02, T03, T04) for the preset charging speed matrix T0, where T01 is the first preset charging speed, T02 is the second preset charging speed, T03 is the third preset charging speed, T04 is the fourth preset charging speed, and T01 < T02 < T03 < T04. The processing module is used to select a corresponding remaining life time based on the relationship between vd and the preset charging speed matrix T0 as the predicted life of the vehicle battery. When vd < T01, select the fourth preset remaining life time A4 as the predicted life of the vehicle battery. When T01 ≤ vd < T02, select the third preset remaining life time A3 as the predicted life of the vehicle battery. When T02 ≤ vd < T03, select the second preset remaining life time A2 as the predicted life of the vehicle battery. When T03 ≤ vd < T04, select the first preset remaining life time A1 as the predicted life of the vehicle battery. A monitoring system for the cooperation state between a vehicle and a charging stand in an electric vehicle charging and discharging process is characterized by this.

2. The monitoring module determines the charging and discharging state of the vehicle battery based on a preset formula, which includes calculating the remaining amount of the vehicle battery based on the battery terminal voltage Vd and the battery charging and discharging current Id, and determining the charging and discharging state of the vehicle battery based on the remaining amount of the vehicle battery. The preset formula is as follows. 【Number 1】 where SOC is the remaining amount of the vehicle battery, V ｂａｔ is the terminal voltage of the vehicle battery, V ｍｉｎ is the minimum allowable voltage of the vehicle battery, V ｍａｘ is the maximum allowable voltage of the vehicle battery, and When the remaining amount of the vehicle battery exceeds 50%, it is determined that the vehicle battery is in a charging state. When the remaining amount of the vehicle battery is less than 50%, it is determined that the vehicle battery is in a discharging state. When the remaining amount of the vehicle battery is 50%, it is determined that the vehicle battery is in a fully charged state. A monitoring system for the cooperation state between a vehicle and a charging stand in an electric vehicle charging and discharging process according to Claim 1 is characterized by this.

3. The processing module is also used to obtain the past charging times k of the vehicle battery. The processing module is also set with a preset past charge count matrix R0 and a preset remaining life time correction coefficient matrix B. For the preset remaining life time correction coefficient matrix B, B(B1, B2, B3, B4) is set. Here, B1 is the first preset remaining life time correction coefficient, B2 is the second preset remaining life time correction coefficient, B3 is the third preset remaining life time correction coefficient, B4 is the fourth preset remaining life time correction coefficient, and 0.6 < B1 < B2 < B3 < B4 < 1. For the preset past charge count matrix R0, R0(R01, R02, R03, R04) is set. Here, R01 is the first preset past charge count, R02 is the second preset past charge count, R03 is the third preset past charge count, R04 is the fourth preset past charge count, and R01 < R02 < R03 < R04. The processing module is used to select a corresponding remaining life time correction coefficient based on the relationship between k and the preset past charge count matrix R0 to correct the remaining life time. When k < R01, the fourth preset remaining life time correction coefficient B4 is selected to correct the fourth preset remaining life time A4, and the corrected remaining life time is A4 × B4. When R01 ≤ k < R02, the third preset remaining life time correction coefficient B3 is selected to correct the third preset remaining life time A3, and the corrected remaining life time is A3 × B3. When R02 ≤ k < R03, the second preset remaining life time correction coefficient B2 is selected to correct the second preset remaining life time A2, and the corrected remaining life time is A2 × B2. When R03 ≤ k < R04, the first preset remaining life time correction coefficient B1 is selected to correct the first preset remaining life time A1, and the corrected remaining life time is A1 × B1. A monitoring system for the cooperative state of a vehicle and a charging stand in an electric vehicle charging and discharging process according to claim 1, characterized in that.

4. When it is determined that the vehicle battery is in a discharging state, the monitoring module is further used to generate a discharge curve based on the state parameters of the vehicle battery and the remaining amount of the vehicle battery, and to obtain the discharge speed vt of the vehicle battery based on the discharge curve. The processing module is further set with a preset discharge rate matrix W0 and a preset remaining life time quadratic correction coefficient matrix C. For the preset remaining life time quadratic correction coefficient matrix C, C (C1, C2, C3, C4) is set. Here, C1 is the first preset remaining life time quadratic correction coefficient, C2 is the second preset remaining life time quadratic correction coefficient, C3 is the third preset remaining life time quadratic correction coefficient, C4 is the fourth preset remaining life time quadratic correction coefficient, and 0.6 < C1 < C2 < C3 < C4 < 1. For the preset discharge rate matrix W0, W0 (W01, W02, W03, W04) is set. Here, W01 is the first preset discharge rate, W02 is the second preset discharge rate, W03 is the third preset discharge rate, W04 is the fourth preset discharge rate, and W01 < W02 < W03 < W04. The processing module is further used to select a corresponding remaining life time quadratic correction coefficient based on the relationship between vt and the preset discharge rate matrix W0, and perform quadratic correction on each corrected preset remaining life time. When vt < W01, the fourth preset remaining life time quadratic correction coefficient C4 is selected to perform quadratic correction on the corrected preset fourth remaining life time A4, and the corrected remaining life time is A4 × B4 × C4. When W01 ≤ vt < W02, the third preset remaining life time quadratic correction coefficient C3 is selected to perform quadratic correction on the corrected preset third remaining life time A3, and the corrected remaining life time is A3 × B3 × C3. When W02 ≤ vt < W03, the second preset remaining life time quadratic correction coefficient C2 is selected to perform quadratic correction on the corrected preset second remaining life time A2, and the corrected remaining life time is A2 × B2 × C2. When W03 ≤ vt < W04, the first preset remaining life time quadratic correction coefficient C1 is selected to perform quadratic correction on the corrected preset first remaining life time A1, and the corrected remaining life time is A1 × B1 × C1. A system for monitoring the cooperative state of a vehicle and a charging stand in an electric vehicle charging and discharging process according to claim 3.

5. The processing module is further used to perform a fault prediction on the vehicle battery based on the predicted life of the vehicle battery, where Performing a fault prediction on the vehicle battery by the processing module includes acquiring the past state parameters of the vehicle battery, where the past state parameters of the vehicle battery include the past terminal voltage of the battery, the past charge and discharge current of the battery, and the past temperature of the battery, arranging the acquired past state parameters of the vehicle battery based on time series, and preprocessing the arranged past state parameters, performing learning training on the data in the past state parameters based on SVR to construct a fault prediction model, performing a fault prediction on the vehicle battery based on the fault prediction model and the state parameters of the vehicle battery, executing a residual threshold test and a fault warning based on the fault prediction result of the vehicle battery by the fault prediction model, which is characterized in that the monitoring system for the cooperative state of the vehicle and the charging stand in the electric vehicle charging and discharging process according to claim 4 includes this.

6. Preprocessing the arranged past state parameters by the processing module includes removing the abnormal value data in the past state parameters, performing data interval processing on the data in the past state parameters, and performing a non-linear judgment on the data in the past state parameters to generate a sample format used to support vector machine learning training. The system for monitoring the cooperative state of the vehicle and the charging stand in the electric vehicle charging and discharging process according to claim 5, characterized by this.

7. collecting in real time the vehicle battery state parameters including the battery terminal voltage Vd, the battery charge and discharge current Id, and the battery temperature Td, monitoring in real time the state parameters of the charging stand including the charging stand voltage Vc, the charging stand current Ic, and the charging stand power Pc, transmitting the state parameters of the vehicle battery and the state parameters of the charging stand through wireless communication technology. Receiving the state parameters of the vehicle battery and the state parameters of the charging stand, processing the state parameters of the vehicle battery and the state parameters of the charging stand, and determining the charge and discharge state of the vehicle battery based on a preset formula; When it is determined that the vehicle battery is in a charged state, a charging curve is generated based on the state parameters of the charging stand, and it is also used to obtain the charging speed vd of the vehicle battery based on the charging curve; Including predicting the life of the vehicle battery based on the charge and discharge state of the vehicle battery, the state parameters of the vehicle battery, and the state parameters of the charging stand; Specifically, predicting the life of the vehicle battery based on the charging speed vd of the vehicle battery includes: Presetting a preset charging speed matrix T0 and a preset remaining life time matrix A, and setting A(A1, A2, A3, A4) for the preset remaining life time matrix A, where A1 is the first preset remaining life time, A2 is the second preset remaining life time, A3 is the third preset remaining life time, A4 is the fourth preset remaining life time, and A1 < A2 < A3 < A4 < 5 years; Setting T0(T01, T02, T03, T04) for the preset charging speed matrix T0, where T01 is the first preset charging speed, T02 is the second preset charging speed, T03 is the third preset charging speed, T04 is the fourth preset charging speed, and T01 < T02 < T03 < T04; Selecting the corresponding remaining life time based on the relationship between vd and the preset charging speed matrix T0 as the predicted life of the vehicle battery; When vd < T01, selecting the fourth preset remaining life time A4 as the predicted life of the vehicle battery; When T01 ≤ vd < T02, selecting the third preset remaining life time A3 as the predicted life of the vehicle battery; When T02 ≤ vd < T03, selecting the second preset remaining life time A2 as the predicted life of the vehicle battery; When T03 ≤ vd < T04, the first preset remaining life time A1 is selected as the predicted life of the vehicle battery. The monitoring method for the cooperative state of the vehicle and the charging stand in the electric vehicle charging and discharging process is applied to the monitoring system for the cooperative state of the vehicle and the charging stand in the electric vehicle charging and discharging process according to any one of claims 1 to 6.

8. Judging the charge and discharge state of the vehicle battery based on a preset formula means that calculating the remaining amount of the vehicle battery based on the battery terminal voltage Vd and the battery charge and discharge current Id, and judging the charge and discharge state of the vehicle battery based on the remaining amount of the vehicle battery, the preset formula is 【Number 2】 where where SOC is the remaining amount of the vehicle battery, V ｂａｔ is the terminal voltage of the vehicle battery, V ｍｉｎ is the minimum allowable voltage of the vehicle battery, V ｍａｘ is the maximum allowable voltage of the vehicle battery, and when the remaining amount of the vehicle battery exceeds 50%, it is judged that the vehicle battery is in a charged state, when the remaining amount of the vehicle battery is less than 50%, it is judged that the vehicle battery is in a discharged state, The monitoring method for the cooperative state of the vehicle and the charging stand in the electric vehicle charging and discharging process according to claim 7, characterized in that when the remaining amount of the vehicle battery is 50%, it is judged that the vehicle battery is in a fully charged state.

9. Further including performing a fault prediction on the vehicle battery based on the predicted life of the vehicle battery, where performing a fault prediction on the vehicle battery includes acquiring the past state parameters of the vehicle battery, and the past state parameters of the vehicle battery include the past terminal voltage of the battery, the past charge and discharge current of the battery, and the past temperature of the battery, arranging the acquired past state parameters of the vehicle battery based on time series, and preprocessing the arranged past state parameters, performing learning training on the data in the past state parameters based on SVR to construct a fault prediction model, performing a fault prediction on the vehicle battery based on the fault prediction model and the state parameters of the vehicle battery, The monitoring method for the cooperative state of the vehicle and the charging stand in the electric vehicle charging and discharging process according to claim 8, characterized in that it includes performing a residual threshold test and a fault warning based on the fault prediction result of the vehicle battery by the fault prediction model.

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