Vehicle electronic single machine quality performance evaluation method and device and medium

Abstract

The invention provides a vehicle electronic single machine quality performance evaluation method and device and a medium, and the method comprises the steps: employing Poisson distribution to build an overrun time interval probability model for vehicle key electronic single machine performance parameters concerning an overrun frequency, and determining a corresponding quality performance evaluation and prediction method; aiming at the vehicle key electronic single machine performance parameters which pay attention to the overrun frequency and the overrun amplitude at the same time, a two-dimensional probability model is adopted for regular modeling of the overrun frequency and the overrun amplitude, and a vehicle key electronic single machine performance parameter overrun two-dimensional probability model is obtained; establishing a multi-level performance evaluation threshold determination method by adopting an over-limit amplitude probability partitioning principle; and performing corresponding quality performance evaluation and prediction based on the vehicle key electronic single machine performance parameter overrun two-dimensional probability model. According to the method, transverse comparison and longitudinal dynamic prediction of the vehicle quality performance can be supported before major tasks, and scientific support is provided for pipe installation and use decision making of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of vehicle performance evaluation technology, and in particular to a method, device and medium for evaluating the quality performance of a vehicle's electronic unit. Background Technology

[0002] With the increasing demands for quality control throughout the product lifecycle, ensuring the safe, reliable, and high-performance operation of systems has become a critical engineering issue. During the service of special vehicles, a large amount of onboard data from key electronic components is collected and recorded in real time, such as battery pack temperature, motor speed, and motor driver current. These performance parameters contain a wealth of product usage information, directly or indirectly reflecting the service quality and performance level of individual components. However, this vast amount of valuable onboard data has not yet been rationally developed and utilized, resulting in significant information waste. Therefore, it is urgent to deeply mine the quality and performance data of key electronic components, understand the evolution patterns of performance parameters, and establish a scientific and reasonable method for evaluating onboard quality and performance.

[0003] In the analysis and research of vehicle performance parameters, it has been found that due to occasional factors such as abnormal user use and unstable product conditions, certain performance parameters occasionally exceed their limits, exhibiting typical characteristics of random events. These few random anomalies often potentially reflect product quality information. For example, during battery pack use, long-term temperature fluctuations and heat accumulation lead to a gradual decline in battery performance. Under high-temperature environments, the coefficient of material expansion increases, exacerbating thermal fatigue. Therefore, analyzing performance parameters such as the maximum temperature of the battery pack to determine if they exceed thresholds can indirectly reflect the battery pack's performance level. Against this backdrop, establishing a probability model for performance parameter exceeding limits and conducting performance evaluation has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention aims to provide a method, apparatus and medium for evaluating the quality performance of vehicle electronic units that overcomes or at least partially solves the above problems.

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

[0006] The first aspect of the present invention provides a method for evaluating the quality performance of a vehicle's electronic components, comprising:

[0007] For key electronic unit performance parameters of vehicles that are of concern regarding the frequency of exceeding limits, a probability model of the time interval of exceeding limits is established using Poisson distribution to conduct the first quality performance evaluation and prediction.

[0008] For the key electronic unit performance parameters of vehicles that simultaneously focus on the frequency and magnitude of exceeding limits, a two-dimensional probability model is used to model the patterns of the frequency and magnitude of exceeding limits, resulting in a two-dimensional probability model of the performance parameters of key electronic units of vehicles exceeding limits.

[0009] The principle of probability zoning for exceeding the limit is adopted to determine the multi-level performance evaluation thresholds;

[0010] Based on the two-dimensional probability model of the vehicle's key electronic unit performance parameters exceeding the limit, a second quality performance evaluation and prediction is performed.

[0011] Optionally, the step of establishing a probability model for the time interval of exceeding limits using a Poisson distribution for the key electronic unit performance parameters of the vehicle, which are of concern regarding the frequency of exceeding limits, and conducting the first quality performance assessment and prediction includes:

[0012] Construct a quality performance index;

[0013] Based on the number of times the performance parameters exceed the damage threshold, predict the probability of the number of times the exceedance occurs within a given time period;

[0014] Predict the quality performance index for the given time period.

[0015] Optionally, the construction quality performance index includes:

[0016] The method for calculating the Quality Performance Index (HI) is as follows:

[0017] HI(T) = 100 - N T *ε

[0018] Where HI∈[0,100], 100 represents perfect health, and N T The number of times the performance exceeds the threshold range, where ε is the deduction coefficient for a single performance overrun.

[0019] Optionally, predicting the probability of the number of times the performance parameter exceeds the damage threshold within a given time period includes:

[0020] Based on the mass performance parameters {X} before time T t Given t = 1, 2, ..., T, determine the number of times N will the performance exceed the limit. T The maximum likelihood estimate of parameter λ is obtained as follows:

[0021]

[0022] Will Replace λ according to formula f ΔT (ΔT) = λexp(-λΔT) calculates the probability distribution of the next performance exceedance time ΔT after time T; where f ΔT (·) represents the probability density function;

[0023] Will Replace λ according to the formula Calculate the probability of the number of times the performance parameter X exceeds the limit within a given time period [T, T+Δt], where P(·) is the probability function and k represents the number of times the performance parameter X exceeds the limit within the time period Δt.

[0024] Optionally, the predicted quality performance index within the given time period includes:

[0025] The predicted quality performance index HI has the same probability of decreasing at time T+Δt as follows:

[0026] HI predict (T+Δt)=HI(T)-k·ε;

[0027] The expected value of the predicted quality performance index is:

[0028]

[0029] The variance of the predicted quality performance index is:

[0030]

[0031] Optionally, for the key electronic unit performance parameters of the vehicle that simultaneously focus on the frequency and magnitude of exceeding limits, a two-dimensional probability model is used to model the patterns of the frequency and magnitude of exceeding limits, resulting in a two-dimensional probability model for the performance parameters of the key electronic unit of the vehicle exceeding limits, including:

[0032] Establish the probability density function for the out-of-limit amplitude:

[0033]

[0034] Where A = X - L0 is the over-limit amplitude, X is the performance parameter, and L0 is the damage threshold;

[0035] Establish the probability density function of the two-dimensional joint distribution:

[0036]

[0037] Where α is the shape parameter and η is the scale parameter.

[0038] Optionally, the step of determining the multi-level performance evaluation threshold by adopting the probability partitioning principle of exceeding the limit amplitude includes:

[0039] Define the probability boundaries for each out-of-limit region;

[0040] Calculate the decrease in quality performance index caused by the over-limit range falling into the over-limit zone of each level.

[0041] Optionally, the probability boundaries for each out-of-limit region include:

[0042] Exceeding Limit Zone 1 (Minor Damage):

[0043] Exceeding Limit Zone 2 (General Damage):

[0044] Exceeding Limit Zone 3 (Moderate Damage):

[0045] Exceeding Limit Zone 4 (Severe Damage):

[0046] Where p1 to p4 represent the probabilities that the specified performance parameter A exceeds the limit and falls into the over-limit zone 1 to 4. Generally, p1 = p2 = p3 = p4 = 0.25. A (A) is the cumulative probability distribution function of the over-limit amplitude A;

[0047] The decrease in quality performance index caused by the calculated over-limit range falling into the over-limit zones at each level includes:

[0048] Through formula Calculate the decrease in quality performance index caused by the over-limit range falling into the over-limit zone of each level;

[0049] Where, β base Given the baseline deduction coefficient, E[A] is the expected total excess, and E[A|p i ] represents the average over-limit amplitude within the over-limit region i, obtained through Calculated.

[0050] Optionally, the second quality performance evaluation and prediction based on the two-dimensional probability model of the vehicle's key electronic unit performance parameters exceeding limits includes:

[0051] Establish a method for calculating the Quality Performance Index (HI):

[0052]

[0053] in, The number of times performance parameter X falls into the over-limit region i at time T;

[0054] Performance index HI at time T+Δt predict Monte Carlo predictions for (T+Δt) include:

[0055] S1, from the two-dimensional joint probability distribution function The operation of generating a set of random numbers, denoted as (ΔT1, A1), wherein... The maximum likelihood estimate of the probability density function of the two-dimensional joint distribution.

[0056] S2, Repeat step S1 until m+1 random numbers are generated that satisfy... Determine A1~A m The number of items falling into the aforementioned over-limit regions 1 to 4 are respectively denoted as satisfy remember If Δt ≤ ΔT1, then directly denote it as...

[0057] S3, Repeat steps S1 and S2 a preset number of times, and record the results. And sorted as

[0058] S4, predicting HI predict The mean of (T+Δt):

[0059]

[0060] Predicting HI predict Variance of (T+Δt):

[0061]

[0062] Predicting HI predict The cumulative distribution function of (T+Δt):

[0063]

[0064] A second aspect of the present invention provides a vehicle electronic unit quality performance evaluation device, comprising: a processor and a memory;

[0065] The memory is used to store computer programs;

[0066] The processor is used to execute the vehicle electronic unit quality performance evaluation method as described above by calling the computer program.

[0067] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the vehicle electronic unit quality performance evaluation method as described above.

[0068] Therefore, the vehicle electronic single-unit quality performance evaluation method, device and medium provided by the present invention can perform probabilistic modeling and evaluation of performance parameter over-limit events (including over-limit frequency and over-limit magnitude), and realize the construction of multi-level performance thresholds and quality performance prediction based on probability. This enables the horizontal comparison and vertical dynamic prediction of vehicle quality performance before major tasks, and provides scientific support for its management, installation and use decisions. Attached Figure Description

[0069] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 A flowchart of a vehicle electronic unit quality performance evaluation method provided in an embodiment of the present invention;

[0071] Figure 2 A specific example flowchart of a vehicle electronic unit quality performance evaluation method provided in an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of a quality performance index degradation model based on excessive frequency provided in an embodiment of the present invention;

[0073] Figure 4 This is a schematic diagram of a quality performance index degradation model based on the frequency and magnitude of exceeding limits, provided in an embodiment of the present invention.

[0074] Figure 5 This is a schematic diagram of the original temperature monitoring data of a vehicle battery pack provided in an embodiment of the present invention;

[0075] Figure 6 This is a schematic diagram illustrating the evaluation results of the quality performance indicators of a vehicle battery pack provided in an embodiment of the present invention.

[0076] Figure 7 This is a schematic diagram of the exponential distribution fitting results of the over-limit time interval of a vehicle battery pack provided in an embodiment of the present invention.

[0077] Figure 8 A schematic diagram of the Weibull distribution fitting result of the over-limit amplitude of a vehicle battery pack provided in an embodiment of the present invention;

[0078] Figure 9 This is a schematic diagram of the over-limit amplitude partitioning based on the Weibull distribution provided in an embodiment of the present invention;

[0079] Figure 10 This is a schematic diagram illustrating the predicted quality performance indicators of a vehicle battery pack according to an embodiment of the present invention.

[0080] Figure 11 This is a schematic diagram of the structure of the vehicle electronic single-unit quality performance evaluation device provided in an embodiment of the present invention. Detailed Implementation

[0081] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0082] Figure 1 A flowchart of the vehicle electronic unit quality performance evaluation method provided in an embodiment of the present invention is shown. Figure 2 This diagram illustrates a specific example flowchart of a vehicle electronic unit quality performance evaluation method provided by an embodiment of the present invention. (See also...) Figure 1 and Figure 2 The vehicle electronic unit quality performance evaluation method provided in this embodiment of the invention includes:

[0083] S1, for the key electronic unit performance parameters of the vehicle that are of concern regarding the frequency of exceeding limits, a probability model of the time interval of exceeding limits is established using the Poisson distribution to conduct the first quality performance evaluation and prediction;

[0084] S2, For the key electronic unit performance parameters of the vehicle that simultaneously focus on the frequency and magnitude of exceeding limits, a two-dimensional probability model is used to model the regularity of the frequency and magnitude of exceeding limits, resulting in a two-dimensional probability model of the performance parameters of the key electronic unit of the vehicle exceeding limits.

[0085] S3 adopts the principle of probability partitioning of over-limit amplitude to determine the multi-level performance evaluation threshold;

[0086] S4. Based on the two-dimensional probability model of the vehicle's key electronic unit performance parameters exceeding the limit, a second quality performance evaluation and prediction is performed.

[0087] Specifically, the purpose of this invention is to establish a probabilistic model for out-of-limit events of key electronic unit performance parameters in vehicles, and to realize the construction of multi-level performance thresholds based on probability and the evaluation and prediction of quality performance, thereby supporting the horizontal comparison and vertical prediction of the quality performance of key units in vehicles.

[0088] This invention first establishes a temporal probability model for events (including frequency and magnitude of exceeding limits) of key electronic unit performance parameters in vehicles. Based on this model, it provides a method for evaluating and predicting the quality performance of key units. The specific steps are as follows:

[0089] Step 1: For the key electronic unit performance parameters of vehicles that are of concern regarding the frequency of exceeding limits, a probability model of the time interval of exceeding limits is established using the Poisson distribution, and corresponding quality performance evaluation and prediction methods are given.

[0090] Step 2: For key electronic unit performance parameters of vehicles that simultaneously focus on the frequency and magnitude of exceeding limits, a two-dimensional probabilistic model is used to model the patterns of the frequency and magnitude of exceeding limits.

[0091] Step 3: Establish a multi-level performance evaluation threshold determination method by adopting the probability partitioning principle of exceeding the limit amplitude;

[0092] Step 4: Based on the two-dimensional probability model of the out-of-limit performance parameters of key electronic components of the vehicle, a corresponding quality performance evaluation and prediction method is given.

[0093] As an optional implementation of this invention, step S1, which involves establishing a probability model for the time interval of exceeding limits using a Poisson distribution for key electronic unit performance parameters of the vehicle that are of interest in the frequency of exceeding limits, and performing a first quality performance assessment and prediction, includes:

[0094] Construct a quality performance index;

[0095] Based on the number of times the performance parameters exceed the damage threshold, predict the probability of the number of times the exceedance occurs within a given time period;

[0096] Predict the quality performance index for the given time period.

[0097] As an optional implementation of this invention, the construction of the quality performance index includes:

[0098] The method for calculating the Quality Performance Index (HI) is as follows:

[0099] HI(T) = 100 - N T *ε

[0100] Where HI∈[0,100], 100 represents perfect health, and N T The number of times the performance exceeds the threshold range, where ε is the deduction coefficient for a single performance overrun.

[0101] As an optional embodiment of the present invention, predicting the probability of the number of times the performance parameter exceeds the damage threshold within a given time period includes:

[0102] Based on the mass performance parameters {X} before time T t Given t = 1, 2, ..., T, determine the number of times N will the performance exceed the limit. T The maximum likelihood estimate of parameter λ is obtained as follows:

[0103]

[0104] Will Replace λ according to formula f ΔT (ΔT) = λexp(-λΔT) calculates the probability distribution of the next performance exceedance time ΔT after time T; where fΔT (·) represents the probability density function;

[0105] Will Replace λ according to the formula Calculate the probability of the number of times the performance parameter X exceeds the limit within a given time period [T, T+Δt], where P(·) is the probability function and k represents the number of times the performance parameter X exceeds the limit within the time period Δt.

[0106] As an optional embodiment of the present invention, the prediction of the quality performance index within the given time period includes:

[0107] The predicted quality performance index HI has the same probability of decreasing at time T+Δt as follows:

[0108] HI predict (T+Δt)=HI(T)-k·ε;

[0109] The expected value of the predicted quality performance index is:

[0110]

[0111] The variance of the predicted quality performance index is:

[0112]

[0113] In specific implementation, the procedure described in step one, "for vehicle performance parameters of concern regarding the frequency of exceeding limits, a probability model of the time interval for exceeding limits is established using a Poisson distribution, and corresponding quality performance evaluation and prediction methods are provided," is as follows:

[0114] Consider a performance monitoring parameter X of a key electronic unit during vehicle service, such as Figure 3 As shown, it typically operates within a normal range, at which point the damage to a single unit is negligible. However, exceeding this range (referred to as exceeding limits) can cause potential damage to the unit. For example, in a battery pack, the battery's operating state, lifespan, and safety are closely related to temperature. Batteries generate heat during charging and discharging, and the distribution and accumulation of this heat affect the battery's operating state, further leading to thermal fatigue. Therefore, the battery pack's maximum temperature is a critical performance parameter, and analyzing it to evaluate battery pack performance is crucial. To describe this process, this invention constructs a quality performance index HI. Whenever a performance parameter X exceeds a damage threshold L0, the index HI decreases by the same amount as ε, where the damage threshold L0 is given by expert experience.

[0115] Let the time-series data of onboard monitoring performance parameters during vehicle service (such as battery pack temperature, motor speed, and motor driver current) be {X}. t}, t=1,2,...,T. The method for calculating the quality performance index HI is as follows:

[0116] HI(T) = 100 - N T *ε (1)

[0117] Where HI∈[0,100], 100 represents perfect health. N T The number of times the performance exceeds the threshold range is ε, which is the deduction coefficient for a single performance exceedance. According to expert experience, once the quality performance index is reduced to 0, this parameter will no longer be deducted.

[0118] Based on equation (1) and historical operating data of performance parameter X, the quality performance index HI of this parameter can be evaluated in real time. Furthermore, in engineering, there is the issue of how to conduct performance prediction; the corresponding prediction methods are given below.

[0119] As can be seen from the above process, the quality performance index HI depends on the number of performance exceedances; therefore, the quality performance prediction problem is essentially the problem of predicting the number of performance exceedances. Let ΔT be the time interval between two consecutive performance exceedance events, and assume it follows an exponential distribution with memorylessness, i.e.:

[0120] f ΔT (ΔT)=λexp(-λΔT) (2)

[0121] Among them, f ΔT (·) is the probability density function, and λ is the model parameter, which represents the average number of times the performance parameter exceeds the limit per unit time.

[0122] Let Z be the number of times the performance parameter exceeds the limit within a given time period Δt. Δt Then it follows a Poisson distribution Z. Δt ~Poisson(λ·Δt), that is:

[0123]

[0124] Where P(·) is the probability function, and k represents the number of times the performance parameter X exceeds the limit within the time period Δt.

[0125] In actual implementation, firstly, based on the mass performance parameters {X} before time T... t}, t=1,2,...,T, count the number of times its performance exceeds the limit N. T The maximum likelihood estimate of parameter λ is obtained as follows:

[0126]

[0127] Then, Substituting λ into equation (2) allows us to predict the probability distribution of the next performance exceedance time ΔT after time T. Substituting λ into equation (3), the probability of the number of out-of-limit occurrences k = 0, 1, 2, ... within a given time period [T, T+Δt] can be predicted as follows:

[0128]

[0129] That is, the predicted quality performance index HI has the same probability of decreasing at time T+Δt as follows:

[0130] HI predict (T+Δt)=HI(T)-k·ε (6)

[0131] The prediction results are shown in Table 1 below.

[0132] Table 1. Prediction of Quality Performance Index Based on the Number of Performance Exceedances (within the time period [T, T+Δt])

[0133]

[0134]

[0135] Therefore, the expected value and variance of the predicted quality performance index are as follows:

[0136]

[0137] As an optional implementation of this invention, step S2, for the key electronic unit performance parameters of the vehicle that simultaneously focus on the frequency and magnitude of exceeding limits, employs a two-dimensional probability model to model the patterns of the frequency and magnitude of exceeding limits, resulting in a two-dimensional probability model for the performance parameters of the key electronic unit of the vehicle exceeding limits, including:

[0138] Establish the probability density function for the out-of-limit amplitude:

[0139]

[0140] Where A = X - L0 is the over-limit amplitude, X is the performance parameter, and L0 is the damage threshold;

[0141] Establish the probability density function of the two-dimensional joint distribution:

[0142]

[0143] Where α is the shape parameter and η is the scale parameter.

[0144] In specific implementation, the method described in step two, "for the key electronic unit performance parameters of the vehicle that simultaneously focus on the frequency and magnitude of exceeding limits, a two-dimensional probability model is used to model the patterns of the frequency and magnitude of exceeding limits," is as follows:

[0145] Considering a performance parameter X monitored during vehicle service, the quality performance degradation of this parameter still depends on performance parameter exceedance events. However, unlike the quality performance degradation model in step one that only considers the frequency of parameter exceedances and the same decrease in the quality performance index for a single exceedance, this invention further proposes... Figure 4 The quality performance index degradation model shown considers the impact of the performance parameter X on the magnitude of the damage threshold L0 (i.e., the extent of exceeding the limit); that is, the higher the extent of exceeding the limit, the greater the decrease in the quality performance index. Figure 4 Taking the case where the performance parameter is greater than the damage threshold L0 as an example of the out-of-limit region, and the performance parameter is less than the damage threshold L0 as the out-of-limit region, the negative value X can be taken. 处理 = -X or take the reciprocal of X 处理 =1 / X, for performance parameters less than the damage threshold L 0,1 (Lower threshold), greater than the damaging threshold L 0,2 If all (threshold upper limits) are in the out-of-limit region, X can be performed. 处理 =|X-(L 0,1 +L 0,2 ) / 2|,L0=(L 0,2 -L 0,1 ) / 2, thus transforming into Figure 4 The situation is shown below. To conduct based on... Figure 4 The quality performance evaluation and prediction of the model first adopts a two-dimensional probability model to model the regularity of the frequency and magnitude of exceeding the limit.

[0146] When the performance parameter X exceeds the damage threshold L0, the over-limit amplitude is defined as A = X - L0, which is a random variable. This invention assumes that the over-limit amplitude follows a two-parameter Weibull distribution, and its probability density function is:

[0147]

[0148] The cumulative distribution function is:

[0149]

[0150] In the formula, α is the shape parameter and η is the scale parameter. Based on all over-limit amplitude data {A1,A2,...,A...} of the performance parameter X at cutoff time T. N}, we can perform maximum likelihood estimation on α and η, where the likelihood function is:

[0151]

[0152] The log-likelihood function is:

[0153]

[0154] By setting the partial derivatives of the log-likelihood function with respect to α and η to zero, and solving the following equation, we can obtain estimates of the parameters α and η. and

[0155]

[0156] Furthermore, let ΔT be the time interval between two consecutive performance exceedance events. According to step one, its probability density function is:

[0157] f ΔT (ΔT)=λexp(-λΔT) (15)

[0158] Assuming that the marginal probability distributions of the over-limit time interval ΔT and the over-limit amplitude A are independent, the probability density function of the two-dimensional joint distribution can be established as follows:

[0159]

[0160] Its maximum likelihood estimate is:

[0161]

[0162] As an optional implementation of this invention, step S3, which uses the over-limit amplitude probability partitioning principle to determine the multi-level performance evaluation threshold, includes:

[0163] Define the probability boundaries for each out-of-limit region;

[0164] Calculate the decrease in quality performance index caused by the over-limit range falling into the over-limit zone of each level.

[0165] As an optional implementation of this invention, the setting of probability boundaries for each over-limit region includes:

[0166] Exceeding Limit Zone 1 (Minor Damage):

[0167] Exceeding Limit Zone 2 (General Damage):

[0168] Exceeding Limit Zone 3 (Moderate Damage):

[0169] Exceeding Limit Zone 4 (Severe Damage):

[0170] Where p1 to p4 represent the probabilities that the specified performance parameter A exceeds the limit and falls into the over-limit zone 1 to 4. Generally, p1 = p2 = p3 = p4 = 0.25. A (A) is the cumulative probability distribution function of the over-limit amplitude A;

[0171] The decrease in quality performance index caused by the calculated over-limit range falling into the over-limit zones at each level includes:

[0172] Through formula Calculate the decrease in quality performance index caused by the over-limit range falling into the over-limit zone of each level;

[0173] Where, β base Given the baseline deduction coefficient, E[A] is the expected total excess, and E[A|p i ] represents the average over-limit amplitude within the over-limit region i, obtained through Calculated.

[0174] In specific implementation, the "establishment of a multi-level performance evaluation threshold determination method based on the probability partitioning principle of exceeding the limit amplitude" mentioned in step three is carried out as follows:

[0175] like Figure 4 As shown, the performance parameter exceeding the limit area is divided into 4 levels, and the decrease in the quality performance index HI caused by the exceeding parameter falling into each level area is different. The 4 zones can be given by expert experience, or the zone boundaries can be specified according to the probability distribution of the exceeding range established in step 2.

[0176] The probability boundaries for each out-of-limit region are defined as follows:

[0177] Exceeding Limit Zone 1 (Minor Damage):

[0178] Exceeding Limit Zone 2 (General Damage):

[0179] Exceeding Limit Zone 3 (Moderate Damage):

[0180] Exceeding Limit Zone 4 (Severe Damage):

[0181] In the formula, p1 to p4 represent the probabilities that the specified performance parameter A exceeds the limit and falls into the over-limit zone 1 to 4. Generally, p1 = p2 = p3 = p4 = 0.25, F A (A) is the cumulative probability distribution function of the over-limit amplitude A, which can be obtained by the maximum likelihood estimation method in step two using a large amount of performance parameter data of vehicles.

[0182] The decrease in the quality performance index HI caused by the over-limit range A falling into each level of the over-limit zone is as follows:

[0183]

[0184] Where, β base Given the baseline deduction coefficient, E[A] is the expected total excess, and E[A|p i The mean over-limit amplitude under over-limit region i is calculated by the following formula:

[0185]

[0186] In the formula, f A (A) is the probability density function of the over-limit amplitude A, see step two.

[0187] As an optional implementation of this invention, step S4, which involves performing a second quality performance evaluation and prediction based on the two-dimensional probability model of the vehicle's key electronic unit performance parameters exceeding limits, includes:

[0188] Establish a method for calculating the Quality Performance Index (HI):

[0189]

[0190] in, The number of times performance parameter X falls into the over-limit region i at time T;

[0191] Performance index HI at time T+Δt predict Monte Carlo predictions for (T+Δt) include:

[0192] S401, from the two-dimensional joint probability distribution function The operation of generating a set of random numbers, denoted as (ΔT1, A1), wherein... The maximum likelihood estimate of the probability density function of the two-dimensional joint distribution.

[0193] S402, Repeat step S401 until m+1 random numbers are generated that satisfy... Determine A1~A m The number of items falling into the aforementioned over-limit regions 1 to 4 are respectively denoted as satisfy remember If Δt ≤ ΔT1, then directly denote it as...

[0194] S403, Repeat steps S401 and S402 a preset number of times, and record. And sorted as

[0195] S404, predicting HI predict The mean of (T+Δt):

[0196]

[0197] Predicting HI predict Variance of (T+Δt):

[0198]

[0199] Predicting HI predict The cumulative distribution function of (T+Δt):

[0200]

[0201] In specific implementation, the "based on the two-dimensional probability model of exceeding the limits of key electronic unit performance parameters of the vehicle, the corresponding quality performance evaluation and prediction method" mentioned in step four is as follows:

[0202] Based on the two-dimensional probability model of key electronic unit performance parameters exceeding limits established in step two, and the performance exceeding limit partition established in step three, considering the impact of the magnitude of performance parameter exceeding limits on quality performance degradation, the calculation method for the quality performance index HI is established as follows:

[0203]

[0204] in, The number of times performance parameter X falls into the over-limit region i at time T.

[0205] The performance index HI at time T+Δt is given below. predict Monte Carlo prediction method for (T+Δt):

[0206] (1) From the two-dimensional joint probability distribution function given in step two Generate a set of random numbers, denoted as (ΔT1,A1);

[0207] (2) Repeat (1) until m+1 random numbers are generated that satisfy the condition. Statistics A1~A m The number of instances falling into the out-of-limit regions 1 to 4 given in step three are denoted as follows: satisfy remember If Δt ≤ ΔT1, then directly denote it as...

[0208] (3) Repeat (1) and (2) a total of M (usually 10⁵) times, and record the results. And sorted as

[0209] (4) For HI predict The prediction of (T+Δt) includes the following: the predicted mean, variance, and cumulative distribution function:

[0210]

[0211] Therefore, the vehicle electronic unit quality performance evaluation method provided by this invention fully leverages the onboard data information of key vehicle electronic units, introduces random events of onboard performance data exceeding thresholds into quality performance evaluation and prediction, establishes a probability distribution model of performance parameter exceedance frequency, and builds a performance evaluation and prediction method for key units based on this model, providing a more scientific and reasonable explanation for the probabilistic prediction of quality performance. Furthermore, for electronic units that simultaneously focus on the frequency and magnitude of performance exceedances, this invention establishes a two-dimensional probability model and proposes a multi-level performance threshold determination method based on probability distribution, enabling the evaluation and prediction of quality performance indicators. This provides a probabilistic decision-making basis for preventative maintenance, thereby supporting the horizontal comparison and vertical dynamic prediction of the quality performance of key vehicle electronic units, and providing scientific support for their installation and application decisions.

[0212] The following uses the temperature performance parameters of a vehicle battery pack as an example to further illustrate the present invention in detail.

[0213] Collected 5400 data points on the temperature of a vehicle's battery pack over 180 hours. Figure 5 As shown. Taking the normal operating temperature threshold as L0 = 55℃, a total of 27 performance over-limit events were recorded, and the over-limit amplitude data is A. i ={1.33,2.29,0.75,...,0.99}.

[0214] First, probabilistic models for the frequency and magnitude of performance parameter overruns are established using the methods in steps one and two.

[0215] Based on engineering experience, the excess equivalent fraction ε = 0.04 is taken. Based on the collected battery pack temperature data, the battery pack quality performance index HI is evaluated using equation (1). The evaluation results are shown in [the table below]. Figure 6 .

[0216] According to step one, the time interval ΔT between two adjacent performance exceedance events follows an exponential distribution, and the fitting result is as follows: Figure 7 As shown, the calculated estimated values ​​of the distribution parameters are:

[0217]

[0218] The probability density function estimate for the time interval ΔT is:

[0219]

[0220] Given a time period Δt, the number of out-of-limit events Z Δt Then it follows a Poisson distribution, that is:

[0221]

[0222] In addition, according to the over-limit amplitude data A i ={1.33,2.29,0.75,...,0.99}, using the two-parameter Weibull distribution shown in step two for modeling, and performing maximum likelihood estimation of the Weibull distribution parameters according to equations (13) and (14), the parameter estimation results are as follows: and Furthermore, the probability density function of the out-of-limit amplitude distribution can be obtained as follows:

[0223]

[0224] The fitted curve is as follows Figure 8 As shown.

[0225] Then, the method in step three is used to determine the multi-level performance thresholds. Taking p1 = p2 = p3 = p4 = 0.25, the performance parameter exceeding the limit region is divided into 4 levels, and the performance thresholds for the 4 exceeding regions are as follows:

[0226] Exceeding Limit Region 1 (Slight Damage): X∈(L0,L1]=(95,96.66]

[0227] Exceeding Limit Region 2 (Generally Causing Loss): X∈(L1,L2]=(96.66,97.58]

[0228] Exceeding the limit region 3 (moderate damage): X∈(L2,L3]=(97.58,98.65]

[0229] Exceeding limit zone 4 (severe damage): X∈[L3,+∞]=(98.65,+∞](28)

[0230] The partitioning of the overlimited region based on the Weibull distribution model is as follows: Figure 9 As shown.

[0231] Based on the Weibull distribution fitting results, the expected value of the over-limit amplitude can be calculated as E[A] = 2.75. Further calculations using equation (19) yield the expected values ​​of the over-limit amplitude in each over-limit region:

[0232]

[0233] A benchmark deduction coefficient β is set based on engineering experience. base =0.8, and from equation (18), the calculation results of the performance index decrease of each over-limit zone are as follows:

[0234]

[0235] Finally, the quality performance assessment and prediction in step four are carried out. Based on the Monte Carlo sampling method in step four, 105 simulated samplings are conducted to predict the occurrence of out-of-limit events within the next 150 hours. According to equations (21) to (23), the expected value and 90% probability interval of the subsequent quality performance index HI are predicted. The prediction results are as follows: Figure 10 As shown in the figure. Based on this prediction result, further guidance can be provided for subsequent management decisions of the vehicle, supporting the vehicle to complete subsequent tasks with high quality.

[0236] Figure 11 A schematic diagram of the vehicle electronic single-unit quality performance evaluation device provided in an embodiment of the present invention is shown below. Figure 11 The vehicle electronic unit quality performance evaluation device provided in this embodiment of the invention includes: a processor and a memory;

[0237] The memory is used to store computer programs;

[0238] The processor is used to execute the above-described vehicle electronic unit quality performance evaluation method by calling the computer program.

[0239] The vehicle electronic unit quality performance evaluation device 11 can be an electronic device such as a desktop computer, laptop, handheld computer, or cloud server. The vehicle electronic unit quality performance evaluation device 11 may include, but is not limited to, a processor 1101 and a memory 1102. Those skilled in the art will understand that... Figure 11 This is merely an example of the vehicle electronic unit quality performance evaluation device 11 and does not constitute a limitation on the vehicle electronic unit quality performance evaluation device 11. It may include more or fewer components than shown, or different components.

[0240] The processor 1101 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. In this embodiment of the invention, the processor can be the controller described in the above embodiments.

[0241] The memory 1102 can be an internal storage unit of the vehicle electronic unit quality assessment device 11, such as a hard disk or RAM of the vehicle electronic unit quality assessment device 11. The memory 1102 can also be an external storage device of the vehicle electronic unit quality assessment device 11, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the vehicle electronic unit quality assessment device 11. The memory 1102 can also include both internal and external storage units of the vehicle electronic unit quality assessment device 11. The memory 1102 is used to store the computer program 1103 and other programs and data required by the vehicle electronic unit quality assessment device 11.

[0242] Therefore, the vehicle electronic unit quality performance evaluation device provided in this invention fully leverages the onboard data information of key vehicle electronic units, introduces random events of onboard performance data exceeding thresholds into quality performance evaluation and prediction, establishes a probability distribution model for the frequency of performance parameter exceedances, and builds a performance evaluation and prediction method for key units based on this model, providing a more scientific and reasonable explanation for the probabilistic prediction of quality performance. Furthermore, for electronic units that simultaneously focus on the frequency and magnitude of performance exceedances, this invention establishes a two-dimensional probability model and proposes a multi-level performance threshold determination method based on probability distribution, enabling the evaluation and prediction of quality performance indicators. This provides a probabilistic decision-making basis for preventative maintenance, thereby supporting the horizontal comparison and vertical dynamic prediction of the quality performance of key vehicle electronic units, and providing scientific support for their management, installation, and application decisions.

[0243] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for evaluating the quality performance of vehicle electronic components.

[0244] Therefore, by utilizing the computer-readable storage medium provided in this invention, the onboard data information of key electronic units in vehicles is fully explored. Random events of onboard performance data exceeding thresholds are introduced into quality performance evaluation and prediction, establishing a probability distribution model for the frequency of performance parameter exceedances. Based on this, a performance evaluation and prediction method for key units is established, providing a more scientific and reasonable explanation for the probabilistic prediction of quality performance. Furthermore, for electronic units where both the frequency and magnitude of performance exceedances are considered, this invention establishes a two-dimensional probability model and proposes a multi-level performance threshold determination method based on probability distribution. This enables the evaluation and prediction of quality performance indicators, providing a probabilistic decision-making basis for preventative maintenance. Consequently, it supports the horizontal comparison and vertical dynamic prediction of the quality performance of key electronic units in vehicles, providing scientific support for their management, installation, and application decisions.

[0245] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0246] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for evaluating the quality performance of a vehicle's electronic components, characterized in that, include: For key electronic unit performance parameters of vehicles that are of concern regarding the frequency of exceeding limits, a probability model of the time interval of exceeding limits is established using Poisson distribution to conduct the first quality performance evaluation and prediction. For the key electronic unit performance parameters of vehicles that simultaneously focus on the frequency and magnitude of exceeding limits, a two-dimensional probability model is used to model the patterns of the frequency and magnitude of exceeding limits, resulting in a two-dimensional probability model of the performance parameters of key electronic units of vehicles exceeding limits. The principle of probability zoning for exceeding the limit is adopted to determine the multi-level performance evaluation thresholds; Based on the two-dimensional probability model of the vehicle's key electronic unit performance parameters exceeding the limit, a second quality performance evaluation and prediction is performed.

2. The method according to claim 1, characterized in that, The first quality performance assessment and prediction, which focuses on the key electronic unit performance parameters of vehicles with high frequency of exceeding limits, uses a Poisson distribution to establish a probability model for the time interval of exceeding limits, including: Construct a quality performance index; Based on the number of times the performance parameters exceed the damage threshold, predict the probability of the number of times the exceedance occurs within a given time period; Predict the quality performance index for the given time period.

3. The method according to claim 2, characterized in that, The constructed quality performance index includes: The method for calculating the Quality Performance Index (HI) is as follows: HI(T)=100-N T *ε Where HI∈[0,100], 100 represents perfect health, and N T The number of times the performance exceeds the threshold range, where ε is the deduction coefficient for a single performance overrun.

4. The method according to claim 3, characterized in that, The method of predicting the probability of the number of times the performance parameter exceeds the damage threshold within a given time period includes: Based on the mass performance parameters {X} before time T t Given t = 1, 2, ..., T, determine the number of times N will the performance exceed the limit. T The maximum likelihood estimate of parameter λ is obtained as follows: Will Replace λ according to formula f ΔT (ΔT) = λexp(-λΔT) calculates the probability distribution of the next performance exceedance time ΔT after time T; where f ΔT (·) represents the probability density function; Will Replace λ according to the formula Calculate the probability of the number of times the performance parameter X exceeds the limit within a given time period [T,T+Δt], where P(·) is the probability function and k represents the number of times the performance parameter X exceeds the limit within the time period Δt.

5. The method according to claim 4, characterized in that, The predicted quality performance index for the given time period includes: The predicted quality performance index HI has the same probability of decreasing at time T+Δt as follows: HI predict (T+Δt)=HI(T)-k·ε; The expected value of the predicted quality performance index is: The variance of the predicted quality performance index is:

6. The method according to claim 5, characterized in that, For the key electronic unit performance parameters of vehicles that simultaneously focus on the frequency and magnitude of exceeding limits, a two-dimensional probabilistic model is used to model the patterns of the frequency and magnitude of exceeding limits. The resulting two-dimensional probabilistic model for exceeding limits of key electronic unit performance parameters of vehicles includes: Establish the probability density function for the out-of-limit amplitude: Where A = X - L0 is the over-limit amplitude, X is the performance parameter, and L0 is the damage threshold; Establish the probability density function of the two-dimensional joint distribution: Where α is the shape parameter and η is the scale parameter.

7. The method according to claim 6, characterized in that, The method of using the probability partitioning principle for exceeding the limit amplitude to determine the multi-level performance evaluation thresholds includes: Define the probability boundaries for each out-of-limit region; Calculate the decrease in quality performance index caused by the over-limit range falling into the over-limit zone of each level.

8. The method according to claim 7, characterized in that, The probability boundaries for each out-of-limit region include: Exceeding Limit Zone 1 (Minor Damage): Exceeding Limit Zone 2 (General Damage): Exceeding Limit Zone 3 (Moderate Damage): Exceeding Limit Zone 4 (Severe Damage): Where p1 to p4 represent the probabilities that the specified performance parameter A exceeds the limit and falls into the over-limit zone 1 to 4. Generally, p1 = p2 = p3 = p4 = 0.

25. A (A) is the cumulative probability distribution function of the over-limit amplitude A; The decrease in quality performance index caused by the calculated over-limit range falling into the over-limit zones at each level includes: Through formula Calculate the decrease in quality performance index caused by the over-limit range falling into the over-limit zone of each level; Where, β base Given the baseline deduction coefficient, E[A] is the expected total excess, and E[A|p i ] represents the average over-limit amplitude within the over-limit region i, obtained through Calculated.

9. The method according to claim 8, characterized in that, The second quality performance evaluation and prediction based on the two-dimensional probability model of the out-of-limit performance parameters of the vehicle's key electronic components includes: Establish a method for calculating the Quality Performance Index (HI): in, The number of times performance parameter X falls into the over-limit region i at time T; Performance index HI at time T+Δt predict Monte Carlo predictions for (T+Δt) include: S1, from the two-dimensional joint probability distribution function The operation of generating a set of random numbers, denoted as (ΔT1, A1), wherein... The maximum likelihood estimate of the probability density function of the two-dimensional joint distribution. S2, Repeat step S1 until m+1 random numbers are generated that satisfy... Determine A1~A m The number of elements falling into the aforementioned over-limit regions 1 to 4 is denoted as NΔti, and satisfies the following conditions: remember If Δt ≤ ΔT1, then directly denote it as... S3, Repeat steps S1 and S2 a preset number of times, and record the results. And sorted as S4, predicting HI predict The mean of (T+Δt): Predicting HI predict Variance of (T+Δt): Predicting HI predict The cumulative distribution function of (T+Δt):

10. A vehicle electronic unit quality performance evaluation device, characterized in that, include: Processor, memory; The memory is used to store computer programs; The processor is configured to execute the vehicle electronic unit quality performance evaluation method as described in any one of claims 1 to 9 by invoking the computer program.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the vehicle electronic unit quality performance evaluation method according to any one of claims 1 to 9.

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