Electric vehicle off-board charger loss rate over-limit risk assessment method and system

Abstract

The invention belongs to the technical field of charging pile maintenance, and particularly relates to a loss rate overrun risk assessment method and system for an off-board charger of an electric vehicle, and the method comprises the steps: building a real-time influence component quantification model and a degradation cumulative component quantification model; parameter fitting of the real-time influence component quantitative model is realized based on the environment temperature time sequence data and the loss rate time sequence data, and parameter fitting of the degradation accumulated component quantitative model is realized based on the environment temperature time sequence data, the environment humidity time sequence data and the loss rate time sequence 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, and performing loss rate over-limit risk assessment based on the loss rate prediction model. According to the method, the loss rate long-term trend prediction and probabilistic risk early warning for the operation and maintenance scene of the charging pile can be realized, the influence of various external factors on the loss rate is fully considered in the model, and the model is high in refinement degree.

Description

Technical Field

[0001] This invention belongs to the field of charging pile maintenance technology, specifically relating to a method and system for assessing the risk of excessive loss rate of off-board chargers for electric vehicles. Background Technology

[0002] The loss rate of DC charging piles, as one of the core indicators for measuring their energy efficiency and operational economy, has a crucial impact on the performance evaluation and operating cost control of charging piles. However, the current verification procedure for DC charging piles involves verifying the chargers every three years, which suffers from drawbacks such as low efficiency, poor timeliness, and high cost. If the charger loss rate is abnormal between two verification periods, existing methods cannot detect the abnormal chargers in time, which will seriously affect the operational economy of DC charging piles; at the same time, it cannot detect potential risks such as aging and failure of charger components in a timely manner, endangering personal and property safety.

[0003] Patent application number 202111178722.2 provides a method for predicting the cumulative wear life of rechargeable batteries considering operating conditions. It uses a degradation model to predict the cumulative wear life, which considers the impact of operating conditions (such as temperature and humidity) on the degradation process. However, this method is trained using a data-driven approach, making it difficult to understand the influence of different factors on the degradation process, and its interpretability is insufficient in complex stress coupling modeling. Patent application number 202411314692.7 provides a method for predicting the remaining life of motor insulation. Based on the remaining breakdown voltage, it establishes a motor insulation degradation model based on the Wiener equation and the Arrhenius formula, and then designs a remaining life prediction model, which considers the aging effect of temperature on insulation life. However, the above methods only consider the aging effect of temperature and do not take into account the potential insulation breakdown caused by real-time effects. Therefore, accurately grasping the loss rate of DC charging piles and its development trend over time, so as to reasonably arrange the inspection plan and provide timely warnings, has important practical significance. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method and system for assessing the risk of loss rate exceeding limits of off-board chargers for electric vehicles. This method and system can achieve long-term trend prediction of loss rate for charging pile operation and maintenance scenarios, as well as probabilistic risk warning, and fully considers the impact of various external factors on the loss rate. The model has a high degree of refinement.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention provides a method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles, characterized in that:

[0007] The method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles includes:

[0008] 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.

[0009] Step 2: Establish a real-time impact component quantification model and a degradation cumulative component quantification model. Based on time-series data of ambient temperature and loss rate, perform parameter fitting on the real-time impact component quantification model. Based on time-series data of ambient temperature, ambient humidity, and loss rate, perform parameter fitting on the degradation cumulative component quantification model. Based on the fitted degradation cumulative component quantification model and the real-time impact component quantification model, construct a loss rate prediction model.

[0010] Step 3: Assess the risk of exceeding the loss rate limit based on the loss rate prediction model.

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

[0012] ;

[0013] In the above formula, This represents the operating cycle output by the loss rate prediction model. Loss rate; Indicates the running cycle The real-time impact component of the last sub-time is calculated by the real-time impact component quantization model; Indicates the running cycle The degradation cumulative component of the last sub-time is calculated by the degradation cumulative component quantization model;

[0014] The expression for the real-time influence component quantization model is:

[0015] ;

[0016] In the above formula, express The real-time impact of time. Time indicates the operating cycle The first in At this moment, express Temperature at any moment; , All of these parameters affect the component quantization model in real time. 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 parameters of the degradation cumulative component quantization model are fitted according to the following steps:

[0023] Calculate the loss rate increment using the following formula:

[0024] ;

[0025] 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;

[0026] make Real-time impact component increment at any moment ,but It satisfies the following statistical distribution:

[0027] ;

[0028] Then, based on the above statistical distribution, a joint maximum likelihood function is established:

[0029] ;

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

[0031] Taking the partial derivative of the joint maximum likelihood function, we get , The expression for the estimated value:

[0032] ;

[0033] ;

[0034] In the above formula, , They are respectively , Estimated value;

[0035] Based on the fitted real-time impact component quantization model, the time series data of the second real-time impact component is obtained. Increments are taken from both the loss rate time series data and the second real-time impact component time series data to obtain loss rate increment time series data and real-time impact component increment time series data. These obtained loss rate increment time series data and real-time impact component increment time series data are then substituted into... , From the expression for the estimated value, we get , Estimated value.

[0036] The parameters of the real-time influence component quantization model are fitted according to the following steps:

[0037] The first real-time influence component time-series data is obtained by subtracting the initial loss rate from the loss rate time-series data. Based on the first real-time influence component time-series data and the ambient temperature time-series data, the parameters of the real-time influence component quantification model are fitted using the nonlinear least squares method.

[0038] Step three specifically involves: obtaining the loss rate probability density function based on the loss rate prediction model, calculating the probability of loss rate exceeding the limit based on the loss rate probability density function, and using the probability of loss rate exceeding the limit to assess the risk of loss rate exceeding the limit; the expression for the loss rate probability density function is:

[0039] ;

[0040] In the above formula, Let the loss rate probability density function be used. Indicates the forecast period The predicted loss rate; For the prediction period The equivalent duration obtained by performing equivalent time conversion; For the prediction period The real-time impact component of the last sub-time; , These are the parameters in the degradation cumulative component quantization model;

[0041] The formula for calculating the probability of exceeding the loss rate limit is as follows:

[0042] ;

[0043] In the above formula, This represents the probability of the loss rate exceeding the limit. This is the threshold for loss rate exceeding the limit.

[0044] Secondly, the present invention provides a risk assessment system for excessive loss rate of off-board chargers for electric vehicles, the system comprising:

[0045] The risk assessment system for excessive loss rate of off-board chargers for electric vehicles includes:

[0046] The data acquisition and processing module is used to acquire the power time-series data and operating condition time-series data of the DC charging pile, and calculate the loss rate time-series data of the DC charging pile based on the power 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, and the operating condition time-series data includes the ambient temperature time-series data and the ambient humidity time-series data.

[0047] The model building and parameter fitting module is used to establish a real-time impact component quantification model and a degradation cumulative component quantification model. It performs parameter fitting on the real-time impact component quantification model based on environmental temperature time series data and loss rate time series data, and performs parameter fitting on the degradation cumulative component quantification model based on environmental temperature time series data, environmental humidity time series data and loss rate time series data. Based on the fitted degradation cumulative component quantification model and the real-time impact component quantification model, a loss rate prediction model is constructed.

[0048] The loss rate exceeding risk assessment module is used to assess the risk of loss rate exceeding limits based on the loss rate prediction model.

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

[0050] ;

[0051] In the above formula, This represents the operating cycle output by the loss rate prediction model. Loss rate; Indicates the running cycle The real-time impact component of the last sub-time is calculated by the real-time impact component quantization model; Indicates the running cycle The degradation cumulative component of the last sub-time is calculated by the degradation cumulative component quantization model;

[0052] The expression for the real-time influence component quantization model is:

[0053] ;

[0054] In the above formula, express The real-time impact of time. Time indicates the operating cycle The first in 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, , They are respectively , Estimated value;

[0072] Based on the fitted real-time impact component quantization model, the time series data of the second real-time impact component is obtained. Increments are taken from both the loss rate time series data and the second real-time impact component time series data to obtain loss rate increment time series data and real-time impact component increment time series data. These obtained loss rate increment time series data and real-time impact component increment time series data are then substituted into... , From the expression for the estimated value, we get , Estimated value.

[0073] The model building and parameter fitting module is used to perform parameter fitting of the real-time influence component quantization model according to the following steps:

[0074] The first real-time influence component time-series data is obtained by subtracting the initial loss rate from the loss rate time-series data. Based on the first real-time influence component time-series data and the ambient temperature time-series data, the parameters of the real-time influence component quantification model are fitted using the nonlinear least squares method.

[0075] The loss rate exceeding risk assessment module is used to perform loss rate exceeding risk assessment according to the following steps: obtaining the loss rate probability density function based on the loss rate prediction model, calculating the loss rate exceeding probability based on the loss rate probability density function, and using the loss rate exceeding probability to perform loss rate exceeding risk assessment; the expression of the loss rate probability density function is:

[0076] ;

[0077] In the above formula, Let the loss rate probability density function be used. Indicates the forecast period The predicted loss rate; For the prediction period The equivalent duration obtained by performing equivalent time conversion; For the prediction period The real-time impact component of the last sub-time; , These are the parameters in the degradation cumulative component quantization model;

[0078] The formula for calculating the probability of exceeding the loss rate limit is as follows:

[0079] ;

[0080] In the above formula, This represents the probability of the loss rate exceeding the limit. This is the threshold for loss rate exceeding the limit.

[0081] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0082] 1. The method for assessing the risk of loss rate exceeding limits in off-board chargers for electric vehicles described in this invention first establishes a real-time influence component quantification model and a degradation cumulative component quantification model. Then, based on time-series data of ambient temperature and loss rate, the parameters of the real-time influence component quantification model are fitted. Similarly, based on time-series data of ambient temperature, ambient humidity, and loss rate, the parameters of the degradation cumulative component quantification model are fitted. Finally, based on the fitted degradation cumulative component quantification model and the real-time influence component quantification model, a loss rate prediction model is constructed. The loss rate prediction model is then used to assess the risk of loss rate exceeding limits. This method enables long-term trend prediction of loss rate and probabilistic risk warning for charging pile operation and maintenance scenarios, ensuring the timeliness of the prediction results and solving the problems of low maintenance efficiency and inability to warn of potential risks in existing verification mechanisms. Furthermore, in the constructed loss rate prediction model, the loss rate is divided into a temperature-sensitive real-time influence component and a degradation cumulative component with long-term influence characteristics, fully considering the impact of various external factors on the loss rate and improving the model's granularity.

[0083] 2. The method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles described in this invention, on the one hand, establishes a real-time influence component quantification model based on the strong temperature sensitivity of loss, and on the other hand, establishes a degradation cumulative component model by combining the Wiener process and the acceleration factor. The Wiener process describes the influence of various random factors in the loss rate degradation process, while the acceleration factor quantifies the accelerating effect of electrical, thermal, and humidity stresses on degradation through equivalent time conversion. The parameters of the degradation cumulative component model are estimated and solved using the maximum likelihood method. Based on the above specific real-time influence component quantification model and the specific degradation cumulative component model, a loss rate prediction model is constructed, which can improve the accuracy of the prediction results. Attached Figure Description

[0084] Figure 1 This is a flowchart of the method described in this invention.

[0085] Figure 2 This is a structural block diagram of the system described in this invention. Detailed Implementation

[0086] The present invention will now be described in further detail with reference to specific embodiments and accompanying 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, express The real-time impact components at each moment can be used to calculate the operating cycle through this model. The real-time impact components corresponding to each sub-time point; This refers to the initial loss rate under standard operating conditions. Under standard operating conditions of 20℃ and 50% relative humidity, the initial loss rate is generally 5%-8%. Reference temperature; , All of these parameters affect the component quantization model in real time. express Temperature at any moment;

[0098] A degradation cumulative component quantization model is established based on the drift Wiener process and acceleration factors. This model describes the accelerating effects of ambient temperature, humidity, and electrical stress during operation on loss performance degradation. Specifically, the expression for the degradation cumulative component quantization model is as follows:

[0099] ;

[0100] In the above formula, Indicates the running cycle The cumulative degradation component of the last sub-time; 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 running cycle The sub-time contained within The number of; , All of these are parameters in the degenerate cumulative component quantization model; , They are respectively for time, The standard Wiener process corresponding to the equivalent time conversion at each moment; it should be noted that when The upper limit of the value is The model calculates the last sub-time point, i.e., the entire running cycle. The cumulative degradation component, when The upper limit of the value is ,and Then the model calculates the first... The cumulative degradation component at each sub-time point, therefore, the running cycle can be calculated using this model. The cumulative degradation component corresponding to each sub-time point;

[0101] Acceleration factors for all sub-times are calculated based on specified reference temperature, reference humidity, and rated power. Then, equivalent time is calculated using the following formula based on these acceleration factors. ,calculate A lifetime acceleration model is introduced, and environmental temperature, humidity, and electrical stress during charging pile operation are combined to more accurately describe the nonlinear degradation process:

[0102] ;

[0103] ;

[0104] In the above formula, For the first The acceleration factor corresponding to each sub-moment; For the first The duration of each sub-moment , , respectively running cycle The first The, the Individual moments; , , The first Temperature, humidity, and average power at each instant; , , These are the reference temperature, reference humidity, and rated power, respectively. , All are power exponent parameters, with empirical values ​​of 2-3; To activate the energy, an empirical value of 0.5-1 eV is used; The Boltzmann constant is taken as an empirical value of 8.617385 × 10⁻⁵ eV / 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; , They are respectively time, The real-time impact of each moment; , They are respectively time, Temperature at any moment;

[0112] make Real-time impact component increment at any moment ,but It satisfies the following statistical distribution:

[0113] ;

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

[0115] ;

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

[0117] Taking the partial derivative of the joint maximum likelihood function, we get , The expression for the estimated value:

[0118] ;

[0119] ;

[0120] In the above formula, , They are respectively , Estimated value;

[0121] Based on the fitted real-time impact component quantization model, the time series data of the second real-time impact component is obtained. Increments are taken from both the loss rate time series data and the second real-time impact component time series data to obtain loss rate increment time series data and real-time impact component increment time series data. These obtained loss rate increment time series data and real-time impact component increment time series data are then substituted into... , From the expression for the estimated value, we get , Estimated value;

[0122] A loss rate prediction model is established by combining the well-fitted real-time impact component quantization model and the degradation cumulative component quantization model. Specifically, the expression of the loss rate prediction model is as follows:

[0123] ;

[0124] In the above formula, This represents the operating cycle output by the loss rate prediction model. Loss rate; Indicates the running cycle The real-time impact component of the last sub-time; Indicates the running cycle The cumulative degradation component of the last sub-time.

[0125] Step 3: Assess the risk of exceeding the loss limit based on the loss rate prediction model; specifically, obtain the probability density function of the loss rate based on the loss rate prediction model, and assess the risk of exceeding the loss limit based on the probability density function of the loss rate; since the predicted loss rate values ​​satisfy the following statistical distribution:

[0126] ;

[0127] In the above formula, Indicates the forecast period The predicted loss rate; For the prediction period The equivalent duration obtained by performing equivalent time conversion; For the prediction period The real-time impact component of the last sub-time;

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

[0129] ;

[0130] In the above formula, Let the loss rate probability density function be used. , These are the parameters in the degradation cumulative component quantization model;

[0131] The formula for calculating the probability of exceeding the loss rate limit is as follows:

[0132] ;

[0133] In the above formula, This represents the probability of the loss rate exceeding the limit. To prevent the loss rate from exceeding the threshold, the operating efficiency of a typical charging pile at half rated power is required to be no less than 90%. Therefore, this is an empirical value. Take 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 building and parameter fitting module is used to establish a real-time impact component quantification model and a degradation cumulative component quantification model. It performs parameter fitting on the real-time impact component quantification model based on ambient temperature time series data and loss rate time series data, and performs parameter fitting on the degradation cumulative component quantification model based on ambient temperature time series data, ambient humidity time series data and loss rate time series data. Based on the fitted degradation cumulative component quantification model and the real-time impact component quantification model, a loss rate prediction model is constructed.

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

[0145] ;

[0146] In the above formula, This represents the operating cycle output by the loss rate prediction model. Loss rate; Indicates the running cycle The real-time impact component of the last sub-time is calculated by the real-time impact component quantization model; Indicates the running cycle The degradation cumulative component of the last sub-time is calculated by the degradation cumulative component quantization model;

[0147] The expression for the real-time influence component quantization model is:

[0148] ;

[0149] In the above formula, express The real-time impact of time. Time indicates the operating cycle The first in At this moment, express Temperature at any moment; , All of these parameters affect the component quantization model in real time. Reference temperature;

[0150] The expression for the degradation cumulative component quantization model is:

[0151] ;

[0152] ;

[0153] ;

[0154] 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; Boltzmann's constant;

[0155] Specifically, the model building and parameter fitting module performs parameter fitting on the degradation cumulative component quantization model according to the following steps:

[0156] First, calculate the loss rate increment using the following formula:

[0157] ;

[0158] 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;

[0159] make Real-time impact component increment at any moment ,but It satisfies the following statistical distribution:

[0160] ;

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

[0162] ;

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

[0164] Taking the partial derivative of the joint maximum likelihood function, we get , The expression for the estimated value:

[0165] ;

[0166] ;

[0167] In the above formula, , They are respectively , Estimated value;

[0168] Based on the fitted real-time impact component quantization model, the time series data of the second real-time impact component is obtained. Increments are taken from both the loss rate time series data and the second real-time impact component time series data to obtain loss rate increment time series data and real-time impact component increment time series data. These obtained loss rate increment time series data and real-time impact component increment time series data are then substituted into... , From the expression for the estimated value, we get , Estimated value;

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

[0170] The loss rate exceeding the limit risk assessment module is used to assess the risk of loss rate exceeding the limit based on the loss rate prediction model. Specifically, the module obtains the loss rate probability density function based on the loss rate prediction model, calculates the probability of loss rate exceeding the limit based on the probability density function, and uses the probability of loss rate exceeding the limit to assess the risk of loss rate exceeding the limit. The expression of the loss rate probability density function is as follows:

[0171] ;

[0172] In the above formula, Let the loss rate probability density function be used. Indicates the forecast period The predicted loss rate; For the prediction period The equivalent duration obtained by performing equivalent time conversion; For the prediction period The real-time impact component of the last sub-time; , These are the parameters in the degradation cumulative component quantization model;

[0173] The formula for calculating the probability of exceeding the loss rate limit is as follows:

[0174] ;

[0175] In the above formula, This represents the probability of the loss rate exceeding the limit. This is the threshold for loss rate exceeding the limit.

[0176] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0178] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles, characterized in that: The method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles includes: Step 1: Collect the power time-series data and operating condition time-series data of the DC charging pile, and calculate the loss rate time-series data of the DC charging pile based on the power time-series data of the DC charging pile; the power data includes the power value time-series data of the AC side input power value and the power value time-series data of the DC side output power value, and the operating condition time-series data includes the ambient temperature time-series data and the ambient humidity time-series data. Step 2: Establish a real-time impact component quantification model and a degradation cumulative component quantification model. Based on time-series data of ambient temperature and loss rate, perform parameter fitting on the real-time impact component quantification model. Based on time-series data of ambient temperature, ambient humidity, and loss rate, perform parameter fitting on the degradation cumulative component quantification model. Based on the fitted degradation cumulative component quantification model and the real-time impact component quantification model, construct a loss rate prediction model. Step 3: Assess the risk of exceeding the loss rate limit based on the loss rate prediction model.

2. The method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles according to claim 1, characterized in that: The expression for the loss rate prediction model is: ； In the above formula, This represents the operating cycle output by the loss rate prediction model. Loss rate; Indicates the running cycle The real-time impact component of the last sub-time is calculated by the real-time impact component quantization model; Indicates the running cycle The degradation cumulative component of the last sub-time is calculated by the degradation cumulative component quantization model; The expression for the real-time influence component quantization model is: ； In the above formula, express The real-time impact of time. Time indicates the operating cycle The first in At this moment, express Temperature at any moment; , All of these parameters affect the component quantization model in real time. Reference temperature; The expression for the degradation cumulative component quantization 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 assessing the risk of excessive loss rate of off-board chargers for electric vehicles according to claim 2, characterized in that: The parameters of the degradation cumulative component quantization model are fitted according to the following steps: Calculate the loss rate increment using the following formula: ； 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; make Real-time impact component increment at any moment ,but It satisfies the following statistical distribution: ； Then, based on the above statistical distribution, a joint maximum likelihood function is established: ； In the above formula, Let be the joint maximum likelihood function of 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 formula, , They are respectively , Estimated value; Based on the fitted real-time impact component quantization model, the time series data of the second real-time impact component is obtained. Increments are taken from both the loss rate time series data and the second real-time impact component time series data to obtain loss rate increment time series data and real-time impact component increment time series data. These obtained loss rate increment time series data and real-time impact component increment time series data are then substituted into... , From the expression for the estimated value, we get , Estimated value.

4. The method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles according to claim 2 or 3, characterized in that: The parameters of the real-time influence component quantization model are fitted according to the following steps: The first real-time influence component time-series data is obtained by subtracting the initial loss rate from the loss rate time-series data. Based on the first real-time influence component time-series data and the ambient temperature time-series data, the parameters of the real-time influence component quantification model are fitted using the nonlinear least squares method.

5. The method for assessing the risk of excessive loss rate of off-board chargers for electric vehicles according to claim 2 or 3, characterized in that: Step three specifically involves: obtaining the loss rate probability density function based on the loss rate prediction model, calculating the probability of loss rate exceeding the limit based on the loss rate probability density function, and using the probability of loss rate exceeding the limit to assess the risk of loss rate exceeding the limit; the expression for the loss rate probability density function is: ； In the above formula, Let the loss rate probability density function be used. Indicates the forecast period The predicted loss rate; For the prediction period The equivalent duration obtained by performing equivalent time conversion; For the prediction period The real-time impact component of the last sub-time; , These are the parameters in the degradation cumulative component quantization model; The formula for calculating the probability of exceeding the loss rate limit is as follows: ； In the above formula, This represents the probability of the loss rate exceeding the limit. This is the threshold for loss rate exceeding the limit.

6. A risk assessment system for excessive loss rate of off-board chargers for electric vehicles, characterized in that: The risk assessment system for excessive loss rate of off-board chargers for electric vehicles includes: The data acquisition and processing module is used to acquire the power time-series data and operating condition time-series data of the DC charging pile, and calculate the loss rate time-series data of the DC charging pile based on the power 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, and the operating condition time-series data includes the ambient temperature time-series data and the ambient humidity time-series data. The model building and parameter fitting module is used to establish a real-time impact component quantification model and a degradation cumulative component quantification model. It performs parameter fitting on the real-time impact component quantification model based on environmental temperature time series data and loss rate time series data, and performs parameter fitting on the degradation cumulative component quantification model based on environmental temperature time series data, environmental humidity time series data and loss rate time series data. Based on the fitted degradation cumulative component quantification model and the real-time impact component quantification model, a loss rate prediction model is constructed. The loss rate exceeding risk assessment module is used to assess the risk of loss rate exceeding limits based on the loss rate prediction model.

7. The risk assessment system for excessive loss rate of off-board chargers for electric vehicles according to claim 6, characterized in that: The expression for the loss rate prediction model is: ； In the above formula, This represents the operating cycle output by the loss rate prediction model. Loss rate; Indicates the running cycle The real-time impact component of the last sub-time is calculated by the real-time impact component quantization model; Indicates the running cycle The degradation cumulative component of the last sub-time is calculated by the degradation cumulative component quantization model; The expression for the real-time influence component quantization model is: ； In the above formula, express The real-time impact of time. Time indicates the operating cycle The first in At this moment, express Temperature at any moment; , All of these parameters affect the component quantization model in real time. Reference temperature; The expression for the degradation cumulative component quantization 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.

8. The risk assessment system for excessive loss rate of off-board chargers for electric vehicles according to claim 7, characterized in that: 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: Calculate the loss rate increment using the following formula: ； 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; make Real-time impact component increment at any moment ,but It satisfies the following statistical distribution: ； Then, based on the above statistical distribution, a joint maximum likelihood function is established: ； In the above formula, Let be the joint maximum likelihood function of 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 formula, , They are respectively , Estimated value; Based on the fitted real-time impact component quantization model, the time series data of the second real-time impact component is obtained. Increments are taken from both the loss rate time series data and the second real-time impact component time series data to obtain loss rate increment time series data and real-time impact component increment time series data. These obtained loss rate increment time series data and real-time impact component increment time series data are then substituted into... , From the expression for the estimated value, we get , Estimated value.

9. The risk assessment system for excessive loss rate of off-board chargers for electric vehicles according to claim 7 or 8, characterized in that: The model building and parameter fitting module is used to perform parameter fitting of the real-time influence component quantization model according to the following steps: The first real-time influence component time-series data is obtained by subtracting the initial loss rate from the loss rate time-series data. Based on the first real-time influence component time-series data and the ambient temperature time-series data, the parameters of the real-time influence component quantification model are fitted using the nonlinear least squares method.

10. The risk assessment system for excessive loss rate of off-board chargers for electric vehicles according to claim 7 or 8, characterized in that: The loss rate exceeding risk assessment module is used to perform loss rate exceeding risk assessment according to the following steps: obtaining the loss rate probability density function based on the loss rate prediction model, calculating the loss rate exceeding probability based on the loss rate probability density function, and using the loss rate exceeding probability to perform loss rate exceeding risk assessment; the expression of the loss rate probability density function is: ； In the above formula, Let the loss rate probability density function be used. Indicates the forecast period The predicted loss rate; For the prediction period The equivalent duration obtained by performing equivalent time conversion; For the prediction period The real-time impact component of the last sub-time; , These are the parameters in the degradation cumulative component quantization model; The formula for calculating the probability of exceeding the loss rate limit is as follows: ； In the above formula, This represents the probability of the loss rate exceeding the limit. This is the threshold for loss rate exceeding the limit.

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