Method and system for evaluating risk of exceeding loss rate of electric vehicle off-board charger

Abstract

The present application belongs to the technical field of charging pile maintenance, and particularly relates to a method and system for evaluating the risk of loss rate exceeding the limit of a non-vehicle-mounted charger of an electric vehicle, which establishes a real-time influence component quantification model and a degradation cumulative component quantification model, realizes parameter fitting of the real-time influence component quantification model based on time series data of environmental temperature and time series data of loss rate, realizes parameter fitting of the degradation cumulative component quantification model based on time series data of environmental temperature, time series data of environmental humidity and time series data of loss rate, constructs a loss rate prediction model based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model, and evaluates the risk of loss rate exceeding the limit based on the loss rate prediction model. The present application can realize long-term trend prediction and probabilistic risk early warning of loss rate for the operation and maintenance scene of charging piles, and fully considers the influence of various external factors on loss rate in the model, and the model has high refinement degree.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of charging pile maintenance, and particularly relates to a method and system for evaluating the over-limit risk of the loss rate of a non-vehicle-mounted charger of an electric vehicle. BACKGROUND

[0002] The loss rate of a direct-current charging pile is one of the core indicators for measuring the energy efficiency and operation economy of the charging pile, and has a key influence on the performance evaluation and operation cost control of the charging pile. However, the current verification regulation for the direct-current charging pile is to carry out verification work on the charger every three years, which has the disadvantages of low efficiency, poor timeliness, high cost and the like. If the loss rate of the charger is abnormal during the two verification periods, the existing method cannot timely find the abnormal charger, which will seriously affect the operation economy of the direct-current charging pile; meanwhile, the existing method cannot timely find potential risks such as aging failure of the charger components, which endangers the safety of persons and property.

[0003] The invention patent with application number 202111178722.2 provides a method for predicting the cumulative loss life of a charging battery considering operating conditions, which predicts the cumulative loss life through a degradation model, and the operating conditions (such as temperature and humidity) are considered in the degradation model to affect the degradation process. However, this method is trained in a data-driven manner, and it is difficult to understand the influence of different factors on the degradation process, and the interpretability is insufficient in complex stress coupling modeling. The invention patent with application number 202411314692.7 provides a method for predicting the residual life of motor insulation, which establishes a motor insulation degradation model based on the Wiener equation and the Arrhenius formula according to the residual breakdown voltage, and then designs a residual life prediction model, and the temperature is considered in the residual life prediction model to affect the aging of the insulation life. However, the above method only considers the influence of temperature on aging, and does not consider the insulation breakdown caused by real-time influence. Therefore, it is of important practical significance to accurately master the loss rate of the direct-current charging pile and its development trend over time, so as to reasonably arrange the verification plan and timely warn. SUMMARY

[0004] The purpose of the present application is to provide a method and system for evaluating the over-limit risk of the loss rate of a non-vehicle-mounted charger of an electric vehicle, which can realize long-term trend prediction of the loss rate for the charging pile operation and maintenance scene, and fully considers the influence of various external factors on the loss rate, and has high model refinement.

[0005] To achieve the above purpose, the technical scheme of the present application is as follows:

[0006] In a first aspect, the present application provides a method for evaluating the over-limit risk of the loss rate of a non-vehicle-mounted charger of an electric vehicle, characterized in that:

[0007] The electric vehicle off-board charger loss rate over-limit risk assessment method comprises:

[0008] Step one, collect the electric energy time series data and working condition time series data of the direct current charging pile, the electric energy data including the electric energy value time series data input from the alternating current side and the electric energy value time series data output from the direct current side, the working condition time series data including the environment temperature time series data and the environment humidity time series data; the loss rate time series data of the direct current charging pile is calculated based on the electric energy time series data of the direct current charging pile;

[0009] Step two, establish a real-time influence component quantification model and a degradation cumulative component quantification model, realize the parameter fitting of the real-time influence component quantification model based on the environment temperature time series data and the loss rate time series data, realize the parameter fitting of the degradation cumulative component quantification model based on the environment temperature time series data, the environment humidity time series data and the loss rate time series data, and construct a loss rate prediction model based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model;

[0010] Step three, perform loss rate over-limit risk assessment based on the loss rate prediction model.

[0011] The expression of the loss rate prediction model is:

[0012] ;

[0013] In the above formula, represents the loss rate output by the loss rate prediction model in the operation period ; represents the real-time influence component at the last sub-moment in the operation period , which is calculated by the real-time influence component quantification model; represents the degradation cumulative component at the last sub-moment in the operation period , which is calculated by the degradation cumulative component quantification model;

[0014] The expression of the real-time influence component quantification model is:

[0015] ;

[0016] In the above formula, represents the real-time influence component at the moment , represents the th sub-moment in the operation period , represents the temperature at the moment ; , are parameters in the real-time influence component quantification model; is the reference temperature;

[0017] The expression for the degradation cumulative component quantization model is:

[0018] ;

[0019] ;

[0020] ;

[0021] In the above formula, Indicates the running cycle The cumulative degradation component of the last sub-time; , All of these are parameters in the degenerate cumulative component quantization model; To monitor the operating cycle The equivalent degradation time obtained by performing equivalent time conversion; , Each is for the running cycle Inner Individual moment, first The equivalent degradation time is obtained by converting each time point into an equivalent time; For the first Acceleration factor at each sub-moment; For the first The duration of each sub-moment , , respectively running cycle Inner The, the Individual moments; For the running cycle Sub-times contained in The number of; , They are respectively for time, The standard Wiener process corresponding to the equivalent time conversion at each moment; , , The first Temperature, humidity, and average power at each instant; , , These are the reference temperature, reference humidity, and rated power, respectively. , All are power-law parameters; To activate energy; is the Boltzmann constant.

[0022] The parameter fitting of the degradation cumulative component quantization model is realized according to the following steps:

[0023] The loss rate increment is calculated according to the following formula:

[0024] ;

[0025] In the above formula, is the loss rate increment from the moment t to the moment t + Δt; is the equivalent time increment; , , are the equivalent degradation times obtained by equivalent time conversion on the moments t, t + Δt respectively; are the standard Wiener processes corresponding to the moments t, t + Δt respectively after equivalent time conversion; are the real-time influence components at the moments t, t + Δt respectively; are the temperatures at the moments t, t + Δt respectively; are the real-time influence component increments at the moments t, t + Δt respectively; are the temperatures at the moments t, t + Δt respectively; ;

[0026] Let the real-time influence component increment at the moment t be ΔI (t) = I (t + Δt) - I (t); then the following statistical distribution is satisfied:

[0027] ;

[0028] Then the joint maximum likelihood function is established according to the above statistical distribution:

[0029] ;

[0030] In the above formula, is the joint maximum likelihood function of the loss rate increment;

[0031] The partial derivative of the joint maximum likelihood function is obtained, and the expression of the estimation value is:

[0032] ;

[0033] ;

[0034] In the above formula, ,​​​​​​​​​​​​​​ are respectively , estimated values;

[0035] Based on the fitted real-time influence component quantification model, the second real-time influence component time series data is obtained, and the increment of the loss rate time series data and the second real-time influence component time series data is taken to obtain the loss rate increment time series data and the real-time influence component increment time series data. The obtained loss rate increment time series data and real-time influence component increment time series data are substituted into the expression of the estimated value to obtain the , estimated value. , estimated value.

[0036] The parameter fitting of the real-time influence component quantification model is realized according to the following steps:

[0037] The loss rate time series data is subtracted by the initial loss rate to obtain the first real-time influence component time series data, and the parameters of the real-time influence component quantification model are fitted based on the first real-time influence component time series data and the environmental temperature time series data by using the nonlinear least squares method.

[0038] The step three is specifically: obtaining the loss rate probability density function based on the loss rate prediction model, calculating the loss rate overrun probability according to the loss rate probability density function, and performing loss rate overrun risk assessment by using the loss rate overrun probability; the expression of the loss rate probability density function is:

[0039] ;

[0040] In the above formula, is the loss rate probability density function; represents the loss rate prediction value of the prediction period ; is the equivalent time length obtained by equivalent time conversion for the prediction period ; is the real-time influence component of the last sub-instant within the prediction period ; , are respectively parameters in the degradation cumulative component quantification model;

[0041] The calculation formula of the loss rate overrun probability is:

[0042] ;

[0043] In the above formula, is the loss rate overrun probability; is the loss rate overrun threshold.

[0044] In a second aspect, the present application provides an electric vehicle off-board charger loss rate overrun risk assessment system, comprising:

[0045] The electric vehicle off-board charger loss rate overrun risk assessment system comprises:

[0046] A data acquisition and processing module is configured to acquire electric energy time series data and working condition time series data of the direct current charging pile, and calculate loss rate time series data of the direct current charging pile based on the electric energy time series data of the direct current charging pile. The electric energy data comprises AC side input electric energy value time series data and DC side output electric energy value time series data, and the working condition time series data comprises environment temperature time series data and environment humidity time series data.

[0047] A model construction and parameter fitting module is configured to establish a real-time influence component quantification model and a degradation cumulative component quantification model, perform parameter fitting of the real-time influence component quantification model based on the environment temperature time series data and the loss rate time series data, perform parameter fitting of the degradation cumulative component quantification model based on the environment temperature time series data, the environment humidity time series data and the loss rate time series data, and construct a loss rate prediction model based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model.

[0048] A loss rate overrun risk assessment module is configured to perform loss rate overrun risk assessment based on the loss rate prediction model.

[0049] The expression of the loss rate prediction model is:

[0050] ;

[0051] In the above formula, represents the loss rate output by the loss rate prediction model in the operation period ; represents the real-time influence component at the last sub-moment in the operation period , which is calculated by the real-time influence component quantification model; represents the degradation cumulative component at the last sub-moment in the operation period , which is calculated by the degradation cumulative component quantification model;

[0052] The expression of the real-time influence component quantification model is:

[0053] ;

[0054] In the above formula, represents the real-time influence component at the moment , represents the first moment in the operation period . At this moment, express Temperature at any moment; , All of these parameters affect the component quantization model in real time. Reference temperature;

[0055] The expression for the degradation cumulative component quantization model is:

[0056] ;

[0057] ;

[0058] ;

[0059] In the above formula, Indicates the running cycle The cumulative degradation component of the last sub-time; , All of these are parameters in the degenerate cumulative component quantization model; To monitor the operating cycle The equivalent degradation time obtained by performing equivalent time conversion; , Each is for the running cycle Inner Individual moment, first The equivalent degradation time is obtained by converting each time point into an equivalent time; For the first Acceleration factor at each sub-moment; For the first The duration of each sub-moment , , respectively running cycle Inner The, the Individual moments; For the running cycle Sub-times contained in The number of; , They are respectively for time, The standard Wiener process corresponding to the equivalent time conversion at each moment; , , The first Temperature, humidity, and average power at each instant; , , These are the reference temperature, reference humidity, and rated power, respectively. , All are power-law parameters; To activate energy; is the Boltzmann constant.

[0060] The model building and parameter fitting module is used to fit the parameters of the degenerate cumulative component quantization model according to the following steps:

[0061] ;

[0062] In the above formula, for Time to The increment of the loss rate at any given moment; For equivalent time increments, , , They are respectively for time, The equivalent degradation time obtained by performing equivalent time conversion at any time; , They are respectively for time, The standard Wiener process corresponding to the equivalent time conversion at each moment; , They are respectively time, The real-time impact of each moment; , They are respectively time, Temperature at any moment;

[0063] make Real-time impact component increment at any moment ,but It satisfies the following statistical distribution:

[0064] ;

[0065] Then, based on the above statistical distribution, a joint maximum likelihood function is established:

[0066] ;

[0067] In the above formula, Let be the joint maximum likelihood function of the loss rate increment;

[0068] Taking the partial derivative of the joint maximum likelihood function, we get , The expression for the estimated value:

[0069] ;

[0070] ;

[0071] In the above formula, , respectively are , estimated values;

[0072] Based on the fitted real-time influence component quantification model, second real-time influence component time series data is obtained, and the increment of the loss rate time series data and the second real-time influence component time series data is taken to obtain loss rate increment time series data and real-time influence component increment time series data. The obtained loss rate increment time series data and real-time influence component increment time series data are substituted into the expression of the estimated value to obtain the estimated value. , , estimated value.

[0073] The model construction and parameter fitting module is used to realize parameter fitting of the real-time influence component quantification model according to the following steps:

[0074] The loss rate time series data is subtracted from the initial loss rate to obtain first real-time influence component time series data, and the parameters of the real-time influence component quantification model are fitted based on the first real-time influence component time series data and the environmental temperature time series data using a nonlinear least squares method.

[0075] The loss rate overrun risk assessment module is used to perform loss rate overrun risk assessment according to the following steps: based on the loss rate prediction model, a loss rate probability density function is obtained, a loss rate overrun probability is calculated according to the loss rate probability density function, and loss rate overrun risk assessment is performed using the loss rate overrun probability; the expression of the loss rate probability density function is:

[0076] ;

[0077] In the above formula, is the loss rate probability density function; represents the loss rate prediction value in the prediction period ; is the equivalent time length obtained by equivalent time conversion for the prediction period ; is the real-time influence component of the last sub-instant in the prediction period ; , respectively are parameters in the degradation cumulative component quantification model;

[0078] The calculation formula of the loss rate overrun probability is: ​

[0079] ;

[0080] In the above formula, is the probability of the loss rate exceeding the threshold; is the threshold value of the loss rate exceeding the threshold.

[0081] Compared with the prior art, the present application has the following advantages:

[0082] 1. The method for evaluating the risk of the loss rate of the non-vehicle-mounted charger of the electric vehicle exceeding the threshold, first establishes a real-time influence component quantification model and a degradation cumulative component quantification model, then performs parameter fitting on the real-time influence component quantification model based on the time series data of the environmental temperature and the time series data of the loss rate, performs parameter fitting on the degradation cumulative component quantification model based on the time series data of the environmental temperature, the time series data of the environmental humidity and the time series data of the loss rate, finally constructs a loss rate prediction model based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model, and evaluates the risk of the loss rate exceeding the threshold based on the loss rate prediction model. The above method can realize long-term trend prediction and probabilistic risk early warning of the loss rate for the charging pile operation and maintenance scene, ensures the timeliness of the prediction result, and solves the problems of low maintenance efficiency and inability to early warn potential risks of the existing detection mechanism. In addition, in the constructed loss rate prediction model, the loss rate is divided into a real-time influence component sensitive to temperature and a degradation cumulative component with long-term influence characteristics, various external factors affecting the loss rate are fully considered, and the refinement degree of the model is improved.

[0083] 2. The method for evaluating the risk of the loss rate of the non-vehicle-mounted charger of the electric vehicle exceeding the threshold, on the one hand, establishes a real-time influence component quantification model based on the strong temperature sensitivity of the loss, and on the other hand, establishes a degradation cumulative component model in combination with the Wiener process and the acceleration factor. The Wiener process describes the influence of various random factors in the loss rate degradation process, and the acceleration factor quantifies the acceleration effect of the electric, thermal and humid stresses on the degradation through equivalent time conversion. The parameters of the degradation cumulative component model are estimated and solved by using the maximum likelihood method. The loss rate prediction model is constructed based on the specific real-time influence component quantification model and the specific degradation cumulative component model, and the accuracy of the prediction result can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 is the flowchart of the method of the present application.

[0085] Figure 2 is the structure block diagram of the system of the present application. DETAILED DESCRIPTION

[0086] The present application will be further described in detail below in combination with the specific embodiments and the drawings.

[0087] Example 1:

[0088] See Figure 1 A method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles, comprising the following steps:

[0089] Step 1: Collect the power time-series data and operating condition time-series data of the DC charging pile. The power data includes the power value time-series data input from the AC side and the power value time-series data output from the DC side. The operating condition time-series data includes the ambient temperature time-series data and the ambient humidity time-series data. Calculate the loss rate time-series data of the DC charging pile based on the power time-series data.

[0090] Specifically, the operating cycle Divided into one-hour intervals At each time point, the time series is obtained. Then, the ambient temperature time series data, ambient humidity time series data, AC side input power value time series data, and DC side output power value time series data for all sub-time points are obtained statistically. They represent the first Each time point and its corresponding ambient temperature, ambient humidity, AC input energy value, DC output energy value, and average power; the formula for calculating the average power is:

[0091] ;

[0092] The power time series data is filtered based on the rule that the power output from the DC side must exceed the power output of the DC charging pile at half rated power. The loss rate time series data is then calculated based on the filtered power time series data. The calculation formula for the loss rate time series data is as follows:

[0093] ;

[0094] In the above formula, Obtained from actual calculation The rate of loss at any given moment; , They are respectively The electrical energy input value on the AC side and the electrical energy output value on the DC side of the DC charging pile at any given time; Time indicates the operating cycle The first in Individual moments;

[0095] Step 2: Establish a real-time impact component quantification model. Since the loss rate is most significantly affected by temperature, the real-time impact component quantification model is mainly used to describe the real-time impact of temperature on the loss rate. Specifically, the expression of the real-time impact component quantification model is as follows:

[0096] ;

[0097] In the above formula, represents the real-time influence component at the moment, through which the real-time influence component corresponding to each sub-moment in the running period can be calculated; is the initial loss rate under the standard working condition, the initial loss rate under the standard working condition of temperature 20℃ and relative humidity 50% is generally 5%-8%; is the reference temperature; , are parameters in the real-time influence component quantification model; represents the temperature at the moment;

[0098] According to the drift Wiener process and the acceleration factor, a degradation cumulative component quantification model is established, which is used to describe the accelerated influence of environmental temperature, environmental humidity and electrical stress in operation on the degradation of loss performance; specifically, the expression of the degradation cumulative component quantification model is:

[0099] ;

[0100] In the above formula, represents the degradation cumulative component at the last sub-moment in the running period ; is the equivalent degradation time obtained by equivalent time conversion of the running period ; , are respectively the equivalent degradation times obtained by equivalent time conversion of the first sub-moment and the second sub-moment in the running period ; is the number of sub-moments contained in the running period ; , are both parameters in the degradation cumulative component quantification model; , are respectively the standard Wiener process corresponding to the equivalent time conversion of the moment , ; It should be noted that when the upper limit of is , the degradation cumulative component of the last sub-moment, i.e. the running period , is calculated through the model, and when the upper limit of is , , the degradation cumulative component of the first sub-moment, i.e. the running period , is calculated through the model., and The degradation cumulative component of the first sub-moment is calculated by the model, so the degradation cumulative component corresponding to each sub-moment in the operation period can be calculated by the model;

[0101] The acceleration factor of all sub-moments is calculated based on the specified reference temperature, reference humidity and rated power, and then the equivalent time is converted based on the acceleration factor according to the following formula to obtain , the life acceleration model is introduced at and combined with the environmental temperature, environmental humidity and electrical stress in the charging pile operation process to more accurately describe the nonlinear degradation process:

[0102] ;

[0103] ;

[0104] In the above formula, is the acceleration factor corresponding to the first sub-moment; is the time length of the first sub-moment, , , are the first , the first sub-moment in the operation period ; , , are the temperature, humidity and average power corresponding to the first sub-moment; , , are the reference temperature, reference humidity and rated power; , are both power index parameters, and the empirical value is 2-3; is the activation energy, and the empirical value is 0.5-1eV; is the Boltzmann constant, and the empirical value is 8.617385x10-5eV / K;

[0105] The parameters of the real-time impact component quantification model are fitted based on time-series data of ambient temperature and loss rate. Specifically, the parameters of the real-time impact component quantification model are fitted according to the following steps: First, a moving average is performed on the time-series data of loss rate and ambient temperature to reduce the impact of fluctuations on the fitting. Then, the initial loss rate is subtracted from the moving averaged loss rate time-series data to obtain the time-series data of the first real-time impact component. Next, the time-series data of the first real-time impact component and the moving averaged ambient temperature time-series data are substituted into the real-time impact component quantification model, and the parameters of the real-time impact component quantification model are fitted using the nonlinear least squares method. Taking the time-series data of loss rate as an example, the moving average is explained by taking 2k+1 adjacent data points one by one along the time-series data of loss rate and averaging them. Then, the initial loss rate is subtracted from the average value to obtain the time-series data of the first real-time impact component. The specific calculation formula is as follows:

[0106] ;

[0107] In the above formula, This represents the actual calculated result of the first... The real-time impact of each moment; Indicates the first The average loss rate at each sub-moment; Indicates the first The loss rate at each time step is calculated using the formula for calculating the loss rate time series data.

[0108] The parameters of the degradation cumulative component quantization model are fitted based on time-series data of ambient temperature, ambient humidity, and loss rate. Specifically, the parameters of the degradation cumulative component quantization model are fitted according to the following steps:

[0109] First, calculate the loss rate increment using the following formula:

[0110] ;

[0111] In the above formula, for Time to The increment of the loss rate at any given moment; For equivalent time increments, , , They are respectively for time, The equivalent degradation time obtained by performing equivalent time conversion at any time; , They are respectively for time, The standard Wiener process corresponding to the equivalent time conversion at each moment; , respectively the time, the real-time influence component at the time; , respectively the time, the temperature at the time;

[0112] Let the real-time influence component increment at the time then satisfy the following statistical distribution:

[0113] ;

[0114] Then, according to the above statistical distribution, a joint maximum likelihood function is established:

[0115] ;

[0116] In the above formula, is the joint maximum likelihood function of the loss rate increment;

[0117] The partial derivative of the joint maximum likelihood function is taken to obtain , the expression of the estimation value:

[0118] ;

[0119] ;

[0120] In the above formula, , respectively , the estimation value;

[0121] Based on the fitted real-time influence component quantification model, the second real-time influence component time series data is obtained, and the loss rate time series data and the second real-time influence component time series data are taken as increments to obtain loss rate increment time series data and real-time influence component increment time series data. The obtained loss rate increment time series data and real-time influence component increment time series data are substituted into , the expression of the estimation value, to obtain , the estimation value;

[0122] The loss rate prediction model is established in combination with the fitted real-time influence component quantification model and the degradation cumulative component quantification model. Specifically, the expression of the loss rate prediction model is:

[0123] ;

[0124] In the above formula, denotes the operating period output by the loss rate prediction model ; denotes the operating period ; denotes the operating period ;

[0125] Step three, loss rate over-limit risk assessment based on the loss rate prediction model; specifically, based on the loss rate prediction model, the probability density function of the loss rate is obtained, and the loss rate over-limit risk assessment is carried out based on the probability density function of the loss rate; since the loss rate prediction value satisfies the following statistical distribution:

[0126] ;

[0127] In the above formula, denotes the loss rate prediction value of the prediction period ; is the equivalent time length obtained by equivalent time conversion on the prediction period ; is the real-time influence component of the last sub-instant in the prediction period ;

[0128] Therefore, the expression of the loss rate probability density function is set as:

[0129] ;

[0130] In the above formula, is the loss rate probability density function; , are parameters in the degradation cumulative component quantification model, respectively;

[0131] The calculation formula of the loss rate over-limit probability is:

[0132] ;

[0133] In the above formula, is the loss rate over-limit probability; is the loss rate over-limit threshold value, generally the operating efficiency requirement is not less than 90% under the half rated power of the charging pile, therefore the empirical value is 10%.

[0134] Performance verification:

[0135] The temperature and humidity data within the prediction period are estimated using historical temperature and humidity data of the same period in the region where the charger under test is located. The temperature and humidity data within the prediction period are then input into the loss rate probability density function fitted by the prediction method proposed in this invention to predict the probability of the loss rate of the charger under test exceeding the limit within the prediction period. The results are shown in Table 1.

[0136] Table 1 Prediction results of loss rate exceeding limit probability

[0137]

[0138] A 50% probability of exceeding the loss rate limit indicates a potential anomaly risk for the charging pile. Table 1 shows that the probability of exceeding the loss rate limit reaches 60.33% in the 72nd month, at which point maintenance is deemed necessary. The prediction method proposed in this invention can quantitatively predict the probability of exceeding the loss rate limit for DC charging piles, and the prediction results can accurately describe the degradation trend of the charging piles. Utilizing the probability of exceeding the loss rate limit enables online evaluation of the performance of DC charging piles, solving the problems of low maintenance efficiency and inability to predict potential risks in existing verification mechanisms.

[0139] Example 2:

[0140] See Figure 2 A risk assessment system for excessive loss rate of off-board chargers for electric vehicles includes a data acquisition and processing module, a model construction and parameter fitting module, and a risk assessment module for excessive loss rate. The data acquisition and processing module is used to acquire time-series data of electrical energy and operating conditions from DC charging piles. The electrical energy data includes time-series data of electrical energy values ​​input from the AC side and time-series data of electrical energy values ​​output from the DC side. The operating condition time-series data includes time-series data of ambient temperature and ambient humidity. The data acquisition and processing module is also used to calculate the time-series data of loss rate according to the following formula:

[0141] ;

[0142] In the above formula, For the calculated first Loss rate at each minute; , The first The electrical energy input value on the AC side and the electrical energy output value on the DC side of the DC charging pile at each instant;

[0143] The model construction and parameter fitting module is configured to establish a real-time influence component quantification model and a degradation cumulative component quantification model, perform parameter fitting on the real-time influence component quantification model based on the time-series data of the environmental temperature and the time-series data of the loss rate, perform parameter fitting on the degradation cumulative component quantification model based on the time-series data of the environmental temperature, the time-series data of the environmental humidity and the time-series data of the loss rate, and construct a loss rate prediction model based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model.

[0144] Specifically, the expression of the loss rate prediction model is as follows:

[0145] ;

[0146] In the above expression, represents the loss rate output by the loss rate prediction model in the operation period ; represents the real-time influence component at the last sub-time point in the operation period , which is calculated by the real-time influence component quantification model; represents the degradation cumulative component at the last sub-time point in the operation period , which is calculated by the degradation cumulative component quantification model;

[0147] The expression of the real-time influence component quantification model is as follows:

[0148] ;

[0149] In the above expression, represents the real-time influence component at the time point , represents the th sub-time point in the operation period , represents the temperature at the time point ; , are parameters in the real-time influence component quantification model; is a reference temperature;

[0150] The expression of the degradation cumulative component quantification model is as follows:

[0151] ;

[0152] ;

[0153] ;

[0154] In the above expression, represents the operation period the degradation cumulative component at the last sub-time point; , are parameters in the degradation cumulative component quantification model; is an equivalent degradation time obtained by equivalent time conversion on the operation period ; , are equivalent degradation times obtained by equivalent time conversion on the first sub-time point and the second sub-time point in the operation period ; is an acceleration factor of the first sub-time point; is a time length of the first sub-time point, , , are the first sub-time point and the second sub-time point in the operation period ; , , are the first sub-time point and the second sub-time point in the operation period ; is a number of sub-time points contained in the operation period ; are standard Wiener processes corresponding to the time point and the time point after equivalent time conversion; , are temperatures, humidities and average powers corresponding to the first sub-time point; , , are reference temperature, reference humidity and rated power; , are power index parameters; is an activation energy; is a Boltzmann constant; The model construction and parameter fitting module realizes parameter fitting on the degradation cumulative component quantification model according to the following steps: First, calculate the loss rate increment according to the following formula: ;

[0155] In the formula, ΔL is the loss rate increment from the time point t to the time point t+Δt; Δt is the equivalent time increment, and

[0156] is the loss rate increment from the time point t to the time point t+Δt.

[0157] ;

[0158] In the formula, ΔL is the loss rate increment from the time point t to the time point t+Δt; Δt is the equivalent time increment, and is the loss rate increment from the time point t to the time point t+Δt. is the equivalent time increment, and ​​​​​, , are respectively the equivalent degradation time obtained by equivalent time conversion on the time point , ; , are respectively the standard Wiener process corresponding to the time point , after equivalent time conversion on the time point , are respectively the real-time influence component on the time point , ; , are respectively the temperature on the time point , ;

[0159] Let be the real-time influence component increment on the time point , then satisfy the following statistical distribution:

[0160] ;

[0161] Then, according to the above statistical distribution, a joint maximum likelihood function is established:

[0162] ;

[0163] In the above formula, is the joint maximum likelihood function of the loss rate increment;

[0164] The partial derivative of the joint maximum likelihood function is obtained , The expression of the estimation value is:

[0165] ;

[0166] ;

[0167] In the above formula, , are respectively , estimation values;

[0168] Based on the fitted real-time influence component quantification model, the second real-time influence component time series data is obtained, and the loss rate time series data and the second real-time influence component time series data are taken as increments to obtain loss rate increment time series data and real-time influence component increment time series data. The obtained loss rate increment time series data and real-time influence component increment time series data are substituted into the expression of the estimation value , , to obtain 、 estimated value;

[0169] Specifically, the model construction and parameter fitting module realizes parameter fitting of the real-time influence component quantification model according to the following steps: subtracting the initial loss rate from the loss rate time series data to obtain first real-time influence component time series data, and fitting the parameters of the real-time influence component quantification model based on the first real-time influence component time series data and the environmental temperature time series data using a nonlinear least squares method.

[0170] The loss rate overrun risk assessment module is configured to perform loss rate overrun risk assessment based on the loss rate prediction model. Specifically, the loss rate overrun risk assessment module obtains a loss rate probability density function based on the loss rate prediction model, calculates a loss rate overrun probability according to the loss rate probability density function, and performs loss rate overrun risk assessment using the loss rate overrun probability. The expression of the loss rate probability density function is as follows:

[0171] ;

[0172] In the above formula, is the loss rate probability density function; represents a loss rate prediction value in a prediction period ; is an equivalent time length obtained by performing equivalent time conversion on the prediction period ; is a real-time influence component at the last sub-instant in the prediction period ; 、 are parameters in the degradation cumulative component quantification model, respectively;

[0173] The calculation formula of the loss rate overrun probability is as follows:

[0174] ;

[0175] In the above formula, is the loss rate overrun probability; is a loss rate overrun threshold value.

[0176] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon.

[0177] ​The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0178] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0179] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0180] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing the technical solutions of the present application, but not for limiting it. Although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for evaluating the risk of loss rate overrun of an electric vehicle off-board charger, characterized in that: The method for evaluating the risk of loss rate overrun of an electric vehicle off-board charger comprises: Step 1: collecting electric energy time series data and working condition time series data of a direct current charging pile, and calculating loss rate time series data of the direct current charging pile based on the electric energy time series data; the electric energy data comprises electric energy value time series data input from an alternating current side and electric energy value time series data output from a direct current side, and the working condition time series data comprises environment temperature time series data and environment humidity time series data; Step 2: establishing a real-time influence component quantification model and a degradation cumulative component quantification model, fitting parameters of the real-time influence component quantification model based on the environment temperature time series data and the loss rate time series data, fitting parameters of the degradation cumulative component quantification model based on the environment temperature time series data, the environment humidity time series data and the loss rate time series data, and constructing a loss rate prediction model based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model; Step 3: evaluating the risk of loss rate overrun based on the loss rate prediction model. 2.The method for evaluating the risk of loss rate overrun of an electric vehicle off-board charger according to claim 1, characterized in that: The expression of the loss rate prediction model is: ； In the above formula, represents the running period output by the loss rate prediction model ; represents the running period ; and represents the running period ; and is the initial loss rate under the standard working condition. The expression of the real-time influence component quantification model is: ； In the above formula, represents the real-time influence component at the time point, the time point represents the running period the first sub-time point in the running period, represents the temperature at the time point; , are all parameters in the real-time influence component quantification model; is the reference temperature; The expression of the degradation cumulative component quantification model is: ； ； ； In the above formula, Indicates the running cycle The cumulative degradation component of the last sub-time; , All of these are parameters in the degenerate cumulative component quantization model; To monitor the operating cycle The equivalent degradation time obtained by performing equivalent time conversion; , Each is for the running cycle Inner Individual moment, first The equivalent degradation time is obtained by converting each time point into an equivalent time; For the first Acceleration factor at each sub-moment; For the first The duration of each sub-moment , , respectively running cycle Inner The, the Individual moments; For the running cycle Sub-times contained in The number of; , They are respectively for time, The standard Wiener process corresponding to the equivalent time conversion at each moment; , , The first Temperature, humidity, and average power at each instant; , , These are the reference temperature, reference humidity, and rated power, respectively. , All are power-law parameters; To activate energy; is the Boltzmann constant. 3.The method for evaluating the risk of loss rate overrun of an electric vehicle off-board charger according to claim 2, characterized in that: The fitting of parameters of the degradation cumulative component quantification model is achieved according to the following steps: The loss rate increment is calculated according to the following formula: ； In the above formula, is the increment of the degradation rate at time ; is the equivalent time increment, , , are the equivalent degradation times obtained by equivalent time conversion at time , ; , are the standard Wiener processes corresponding to the equivalent time conversion at time , ; , are the real-time influence components at time , ; , are the temperatures at time , ; Let the real-time influence component increment at the moment then satisfies the following statistical distribution: ； Then the joint maximum likelihood function is established according to the above statistical distribution: ； In the above formula, is the joint maximum likelihood function for the loss rate increment; Taking the partial derivative of the joint maximum likelihood function, we get , The expression for the estimated value: ； ； In the above formulae, , are respectively , estimated values; Based on the fitted real-time influence component quantification model, second real-time influence component time series data is obtained, and the increment of the loss rate time series data and the second real-time influence component time series data is taken to obtain loss rate increment time series data and real-time influence component increment time series data. The obtained loss rate increment time series data and real-time influence component increment time series data are substituted into 、 The expression of the estimated value is obtained 、 The estimated value. 4.The method for evaluating the risk of loss rate overrun of an electric vehicle off-board charger according to claim 2 or 3, characterized in that: The fitting of parameters of the real-time influence component quantification model is achieved according to the following steps: The loss rate time series data is subtracted by the initial loss rate to obtain first real-time influence component time series data, and the parameters of the real-time influence component quantification model are fitted based on the first real-time influence component time series data and the environment temperature time series data by using a nonlinear least squares method. 5.The method for evaluating the risk of loss rate overrun of an electric vehicle off-board charger according to claim 2 or 3, characterized in that: The step three is specifically: obtaining a loss rate probability density function based on the loss rate prediction model, calculating a loss rate overrun probability according to the loss rate probability density function, and evaluating the risk of loss rate overrun by using the loss rate overrun probability; the expression of the loss rate probability density function is: ； In the above formula, is the loss rate probability density function; represents the predicted period of the loss rate prediction value; is the equivalent time length obtained by equivalent time conversion on the predicted period ; is the real-time influence component of the last sub-instant in the predicted period ; , are parameters in the degradation cumulative component quantification model, respectively; The calculation formula of the loss rate overrun probability is: ； In the above formula, is the probability of the loss rate exceeding the threshold value; is the threshold value of the loss rate. 6.A system for evaluating the risk of loss rate overrun of an electric vehicle off-board charger, characterized in that: The system for evaluating the risk of loss rate overrun of an electric vehicle off-board charger comprises: The data acquisition and processing module is configured to acquire electric energy time series data and working condition time series data of the DC charging pile, and calculate loss rate time series data of the DC charging pile based on the electric energy time series data; the electric energy data includes AC side input electric energy value time series data and DC side output electric energy value time series data, and the working condition time series data includes environment temperature time series data and environment humidity time series data; The model construction and parameter fitting module is configured to establish a real-time influence component quantification model and a degradation cumulative component quantification model, perform parameter fitting on the real-time influence component quantification model based on the environment temperature time series data and the loss rate time series data, perform parameter fitting on the degradation cumulative component quantification model based on the environment temperature time series data, the environment humidity time series data and the loss rate time series data, and construct a loss rate prediction model based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model. The loss rate overrun risk assessment module is configured to perform loss rate overrun risk assessment based on the loss rate prediction model.

7. The electric vehicle off-board charger loss rate overrun risk assessment system according to claim 6, wherein: The expression of the loss rate prediction model is: ； In the above formula, represents the operating period output by the loss rate prediction model ; represents the real-time influence component of the last sub-moment in the operating period , which is calculated by the real-time influence component quantification model; represents the degradation cumulative component of the last sub-moment in the operating period , which is calculated by the degradation cumulative component quantification model; is the initial loss rate under the standard working condition; The expression of the real-time influence component quantification model is: ； In the above formula, represents the real-time influence component at the time point, The time point represents the running period The first sub-time point in the period, represents the temperature at the time point; , are parameters in the real-time influence component quantification model; is the reference temperature; The expression of the degradation cumulative component quantification model is: ； ； ； In the above formula, denotes the running period the degradation cumulative component of the last sub-period within the running period , are parameters in the degradation cumulative component quantification model; is the equivalent degradation time obtained by equivalent time conversion of the running period ; , are the equivalent degradation times obtained by equivalent time conversion of the first sub-period and the second sub-period within the running period ; , are the equivalent degradation times obtained by equivalent time conversion of the first sub-period and the second sub-period within the running period ; is the acceleration factor of the first sub-period; is the time length of the first sub-period, , , , are the first sub-period and the second sub-period within the running period ; , are the first sub-period and the second sub-period within the running period ; is the number of sub-periods contained in the running period , are the standard Wiener processes corresponding to the time and the time after equivalent time conversion; , , are the temperature, humidity, and average power corresponding to the first sub-period; , , , are the reference temperature, reference humidity, and rated power; , are power index parameters; is the activation energy; is the Boltzmann constant.

8. The electric vehicle off-board charger loss rate overrun risk assessment system according to claim 7, wherein: The model construction and parameter fitting module is configured to perform parameter fitting on the degradation cumulative component quantification model according to the following steps: Calculate the loss rate increment according to the following formula: ； In the above formula, is the increment of the degradation rate from time to time ; , , are the equivalent degradation times obtained by equivalent time conversion on the time , ; , are the standard Wiener processes corresponding to the time , after equivalent time conversion; , are the real-time influence components of the time , ; , are the temperatures of the time , ; Let Real-time influence component increment of the time instant Then Satisfies the following statistical distribution: ； Then, establish a joint maximum likelihood function according to the above statistical distribution: ； In the above formula, is the joint maximum likelihood function for the loss rate increment; Taking the partial derivative of the joint log-likelihood function, we obtain , the expression for the estimates ； ； In the above formulae, , are respectively , estimated values; Based on the fitted real-time influence component quantification model, second real-time influence component time series data is obtained, and the increment of the loss rate time series data and the second real-time influence component time series data is taken to obtain loss rate increment time series data and real-time influence component increment time series data. The obtained loss rate increment time series data and real-time influence component increment time series data are substituted into , The expression of the estimated value is obtained , The estimated value.

9. The electric vehicle off-board charger loss rate overrun risk assessment system according to claim 7 or 8, wherein: The model construction and parameter fitting module is configured to perform parameter fitting on the real-time influence component quantification model according to the following steps: Subtract the initial loss rate from the loss rate time series data to obtain first real-time influence component time series data, and perform parameter fitting on the real-time influence component quantification model based on the first real-time influence component time series data and the environment temperature time series data by using a nonlinear least squares method.

10. The electric vehicle off-board charger loss rate overrun risk assessment system according to claim 7 or 8, wherein: The loss rate overrun risk assessment module is configured to perform loss rate overrun risk assessment according to the following steps: obtain a loss rate probability density function based on the loss rate prediction model, calculate a loss rate overrun probability based on the loss rate probability density function, and perform loss rate overrun risk assessment by using the loss rate overrun probability; the expression of the loss rate probability density function is: ； In the above formula, is the loss rate probability density function; represents the prediction period loss rate prediction value; is the equivalent time length obtained by equivalent time conversion on the prediction period ; is the real-time influence component of the last sub-instant within the prediction period ; , are parameters in the degradation cumulative component quantification model, respectively; The calculation formula of the loss rate overrun probability is: ； In the above formula, is the probability of exceeding the loss rate; is the threshold value of the loss rate.

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