Automobile thermal management energy efficiency test uncertainty evaluation method and device and storage medium

Abstract

The invention discloses an automobile thermal management energy efficiency test uncertainty evaluation method and device, and a storage medium. The method comprises the following steps: constructing a power battery liquid cooling and heating management system energy efficiency measurement model; probability distribution analysis and abnormal value judgment are carried out on key parameters of the energy efficiency test of the power battery liquid cooling and heating management system; performing Monte Carlo method simulation according to the key parameters of the energy efficiency test of the power battery liquid cooling and heating management system and the energy efficiency measurement model of the power battery liquid cooling and heating management system; and carrying out statistical analysis on EER sample data generated by a Monte Carlo method, and evaluating the uncertainty of an energy efficiency test result. By adopting the technical scheme of the invention, the uncertainty evaluation of the energy efficiency test of the liquid cooling heat dissipation thermal management system of the new energy automobile battery can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of thermal management testing of new energy vehicles, and in particular relates to a method and device for uncertainty assessment of automotive thermal management energy efficiency testing, and a storage medium. Background Art

[0002] New energy vehicles are complex industrial products, consisting of numerous parts and assemblies. The working temperature and material tolerance temperature of each part are different. Only by ensuring that they operate at appropriate temperatures can the safe, efficient and stable operation of the vehicle be guaranteed. The thermal management system of new energy vehicles is based on the system and the whole vehicle, and coordinates the heat of the whole vehicle and the ambient heat to keep each component working in the optimal temperature range. The thermal management of traditional vehicles mainly includes the cooling of the engine and gearbox and the thermal management of the air conditioning system. The thermal management of new energy vehicles includes the thermal management of the motor and electronic control system, the thermal management of the battery system and the thermal management of the passenger compartment air conditioning. The thermal management system of the power battery of new energy vehicles is a key part to ensure that the battery operates within the optimal temperature range. The accuracy of its energy efficiency test is of great significance to the economy and environmental protection of the thermal management of the power battery of new energy vehicles. The accuracy of the test is generally evaluated by measurement uncertainty. Measurement uncertainty is a quantitative evaluation of the credibility or error range of the measurement result, which indicates the reasonable degree of suspicion of the measurement value. It reflects the possible deviation between the measurement result and the true value. Even in the most accurate measurement process, there may still be a certain degree of error or uncertainty. However, the current research on the energy efficiency test and related uncertainty of the power battery thermal management system is not in-depth, especially in the energy efficiency test uncertainty. Since the energy efficiency test involves a variety of complex parameters and sources of uncertainty, these complex nonlinear and multivariable systems are difficult to accurately evaluate their uncertainties using traditional measurement uncertainty assessment methods, such as the GUM method and the Moffat formula. Although these methods are suitable for simple systems, they are limited in dealing with nonlinearity and multivariables and cannot accurately obtain the measurement uncertainty. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for evaluating the uncertainty of automobile thermal management energy efficiency testing, and a storage medium, which can realize the uncertainty evaluation of the energy efficiency testing of the liquid cooling thermal management system of the new energy vehicle battery.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A method for uncertainty assessment of automotive thermal management energy efficiency testing, comprising:

[0006] Step S1, constructing an energy efficiency measurement model for a power battery liquid cooling thermal management system;

[0007] Step S2, performing probability distribution analysis and outlier determination on key parameters of the energy efficiency test of the power battery liquid cooling thermal management system;

[0008] Step S3, performing Monte Carlo simulation according to key parameters of the power battery liquid cooling thermal management system energy efficiency test and the power battery liquid cooling thermal management system energy efficiency measurement model;

[0009] Step S4: Statistically analyze the EER sample data generated by the Monte Carlo method to evaluate the uncertainty of the energy efficiency test results.

[0010] Preferably, in step S1, the energy efficiency measurement model of the power battery liquid cooling thermal management system is:

[0011]

[0012] Among them, Q cooling is the cooling capacity of the system, that is, the amount of heat removed by the thermal management system per unit time; q m is the mass flow rate of the coolant; c is the specific heat capacity of the coolant; T in T is the inlet temperature of the coolant battery pack; out is the outlet temperature of the coolant battery pack; P sys is the power consumption of the system, including the power consumption of the condenser fan, pump, and compressor; P f is the power of the condenser fan; P P is the power of the water pump; P c is the power of the compressor.

[0013] Preferably, in step S2, the key input parameters include: coolant mass flow rate, coolant specific heat capacity, coolant inlet and outlet temperature difference, and system power consumption.

[0014] The present invention also provides an uncertainty assessment device for automobile thermal management energy efficiency testing, comprising:

[0015] A building module for building an energy efficiency measurement model for a power battery liquid cooling thermal management system;

[0016] A processing module is used to perform probability distribution analysis and outlier determination on key parameters of the energy efficiency test of the power battery liquid cooling thermal management system;

[0017] A simulation module, which is used to perform Monte Carlo simulation based on the key parameters of the power battery liquid cooling thermal management system energy efficiency test and the power battery liquid cooling thermal management system energy efficiency measurement model;

[0018] The evaluation module is used to perform statistical analysis on the EER sample data generated by the Monte Carlo method and evaluate the uncertainty of the energy efficiency test results.

[0019] As a preferred embodiment, the energy efficiency measurement model of the power battery liquid cooling thermal management system is:

[0020]

[0021] Among them, Q cooling is the cooling capacity of the system, that is, the amount of heat removed by the thermal management system per unit time; q m is the mass flow rate of the coolant; c is the specific heat capacity of the coolant; T in T is the inlet temperature of the coolant battery pack; out is the outlet temperature of the coolant battery pack; P sys is the power consumption of the system, including the power consumption of the condenser fan, pump, and compressor; P f is the power of the condenser fan; P P is the power of the water pump; P c is the power of the compressor.

[0022] Preferably, the key input parameters include: coolant mass flow rate, coolant specific heat capacity, coolant inlet and outlet temperature difference, and system power consumption.

[0023] The present invention also provides a storage medium, on which a computer program is stored, and when the computer program is running, the method for evaluating the uncertainty of an automobile thermal management energy efficiency test is executed.

[0024] The present invention adopts the Monte Carlo method for uncertainty assessment, which can overcome the limitations of the GUM method in dealing with nonlinear and multivariable systems. Through a large number of random sampling, the Monte Carlo method can accurately evaluate the uncertainty of the energy efficiency of the thermal management system, and is independent of the distribution type of the parameters. The present invention is particularly suitable for complex new energy vehicle power battery thermal management systems, providing more comprehensive and accurate test results, thereby providing strong support for the design optimization and operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0026] Figure 1 This is a simplified schematic diagram of the power battery liquid cooling thermal management system;

[0027] Figure 2 This is a flow chart of an uncertainty assessment method for automotive thermal management energy efficiency testing according to an embodiment of the present invention;

[0028] Figure 3is the EER probability density histogram. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Embodiment 1:

[0032] The liquid cooling thermal management system for the power battery of a new energy vehicle includes two cycles, a refrigeration cycle on the left and a battery cooling cycle on the right. The present invention is only for the battery liquid cooling system, so the evaporator branch is not considered. The system is simplified as follows: Figure 1 As shown, the principle of the power battery liquid cooling thermal management system is as follows:

[0033] (1) The compressor compresses the low-temperature and low-pressure refrigerant gas into a high-temperature and high-pressure gas.

[0034] (2) The condenser cools the high-temperature and high-pressure refrigerant in the external environment, condensing it into a medium-temperature and high-pressure liquid.

[0035] (3) The heat exchanger allows the power battery coolant to exchange heat with the medium-temperature and high-pressure refrigerant in the air-conditioning system. When the medium-temperature and high-pressure refrigerant evaporates in the heat exchanger, it takes away the heat in the power battery coolant and then passes through the compressor to complete a cycle. After being cooled by the heat exchanger, the battery coolant exchanges heat with the battery pack to cool the battery pack.

[0036] The energy efficiency test of power battery liquid cooling thermal management is as follows: The energy efficiency of the power battery liquid cooling thermal management system of new energy vehicles is expressed by the energy efficiency ratio (EER), which is measured by the ratio between the system cooling capacity and the power consumed. The specific formula is:

[0037]

[0038] Q cooling is the cooling capacity of the system, that is, the amount of heat removed by the thermal management system per unit time. It is calculated using the temperature difference between the inlet and outlet of the battery heat exchanger and is calculated using the following formula:

[0039] Q cooling =q m×c×(T in -T out )

[0040] Among them, q m is the mass flow rate of the coolant (kg / s); c is the specific heat capacity of the coolant (J / (kg·K)); T in is the coolant battery pack inlet temperature (K); T out is the coolant battery pack outlet temperature (K).

[0041] P sys is the power consumption of the system, including the power consumption of the condenser fan, pump, compressor, etc. It can be calculated by the following formula:

[0042] P sys =P f +P p +P c

[0043] Among them, P f is the power of the condenser fan (W); P P is the power of the water pump (W); P c is the power of the compressor (W).

[0044] The above parameters are collected as follows Figure 2 As shown, the mass flow rate of the coolant is measured by the mass flow meter of the battery cooling loop; the inlet and outlet temperature difference of the coolant heat exchanger is measured by a platinum resistance thermometer; the power consumption of the condenser, water pump and compressor is obtained by a power meter; the coolant generally adopts a 50% volume concentration of ethylene glycol aqueous solution, and its specific heat capacity can be obtained by checking the ASHRAE manual. Substituting the above values ​​into the EER calculation formula, the energy efficiency of the power battery liquid cooling thermal management system is obtained.

[0045] like Figure 2 As shown, an embodiment of the present invention provides a method for evaluating uncertainty in automotive thermal management energy efficiency testing, comprising:

[0046] Step S1: Establishing an energy efficiency measurement model for a power battery liquid cooling thermal management system

[0047]

[0048] Step S2: Probability distribution analysis of input parameters and outlier determination

[0049] In order to evaluate the uncertainty of the system energy efficiency test, the embodiment of the present invention performs probability distribution analysis on key input parameters (such as coolant mass flow, coolant specific heat capacity, coolant inlet and outlet temperature difference, system power consumption). These input parameters are usually affected by many factors, such as the accuracy of the measuring equipment, the system operating environment, etc. Therefore, before using the Monte Carlo method, reasonable assumptions must be made about the probability distribution of these input parameters.

[0050] Coolant mass flow q m : Based on the measurement error of the flow sensor, the flow measurement data obeys the normal distribution. For example, the mean of the flow measurement data is 0.05kg / s and the standard deviation is 0.002kg / s.

[0051] Temperature measurement: The inlet and outlet temperature difference of the coolant may be affected by the external environment and system conditions, and it is assumed to be normally distributed. Example: Coolant battery pack inlet temperature: Assuming it follows a normal distribution, the mean is 20°C and the standard deviation is 2°C; coolant battery pack outlet temperature: Assuming it follows a normal distribution, the mean is 28°C and the standard deviation is 2°C.

[0052] Specific heat capacity of coolant: The coolant is a 50% volume concentration of ethylene glycol aqueous solution. Its specific heat capacity can be obtained by checking the ASHRAE manual. According to the actual situation and the ASHRAE manual, the specific heat capacity of the coolant is: 3300J / (kg·K)

[0053] System power consumption: System power consumption is mainly composed of the energy consumption of fans, pumps and other auxiliary equipment, which may be affected by factors such as input voltage fluctuations. Fan power: Assume the mean is 50W and the standard deviation is 2W; compressor power: Assume the mean is 600W and the standard deviation is 10W; water pump power: Assume the mean is 50W and the standard deviation is 2W.

[0054] The distribution of each input parameter can be defined by practical experience, experimental data, calibration certificates and the error range and uncertainty provided in the sensor manual.

[0055] As a preference, the Grubbs criterion and the interquartile range method (IQR) are selected for the parameter collection data to determine and remove outliers, wherein the Grubbs criterion is applicable to parameters of normal distribution, and the interquartile range method is applicable to parameters of non-normal distribution. Detailed examples of determining outliers are as follows:

[0056] ① The method of using the Grubbs criterion for outliers is as follows: a set of measured values ​​q of the coolant flow rate mi For example:

[0057]

[0058] Among them, q md is a set of measured values ​​q of the coolant flow ratemi The residual relative to its mean value │v i │The largest one (kg / s); is a set of measured values ​​q of the coolant flow rate mi The average value (kg / s); s is the experimental standard deviation; G(α,n) is the critical value related to the significance level α and the number of repeated measurements n. When the inclusion probability is given as p = 0.99 or p = 0.95, the significance level α (α = 1-p) is 0.01 or 0.05. G(α,n) can be obtained by looking up the table.

[0059] If the above formula is satisfied, then q md is an outlier.

[0060] ② Since the probability distribution of measured data is affected by many factors, other distributions may occur in addition to the normal distribution. For non-normal distribution, the interquartile range method (IQR) can be used to determine outliers. The method is as follows: first, sort the measured values ​​by numerical value, and then calculate the quartiles: the first quartile (Q1) is the value at the 25% position, and the third quartile (Q3) is the value at the 75% position. Then, the IQR is calculated by the difference between the third quartile and the first quartile: IQR = Q3-Q1. Finally, the outliers are determined: data values ​​that are less than (Q1-1.5×IQR) or greater than (Q3+1.5×IQR) are outliers.

[0061] Step S3: Monte Carlo simulation

[0062] The Monte Carlo method is a numerical simulation technique that uses random sampling and probability distribution propagation, and is suitable for uncertainty assessment of complex and nonlinear systems. The present invention performs Monte Carlo simulation by the following steps:

[0063] ① Select the number of Monte Carlo experiments: The number of Monte Carlo experiments is the sample size M, which is related to the shape and inclusion probability of the output probability density distribution. To ensure that the results are statistically significant, the number of simulation experiments is usually selected as 10. 6 .

[0064] ② Random sampling: Based on the probability distribution function of the input parameters established in step S2, M samples (q mr ,T inr ,T outr ,P fr ,P pr ,P cr ,r=1,2,···,M).

[0065] ③ Calculate EER: Substitute the extracted input samples into the energy efficiency measurement model, and then calculate the model at each sample (qmr ,T inr ,T outr ,P fr ,P pr ,P cr ,r=1,2,···,M) the output value EER r =f(q mr ,T inr ,T outr ,P fr ,P pr ,P cr ). The calculated EER r Arrange in non-decreasing order, denoted as EER (r) , let G be the set EER (r) (r=1,2,···,M), then G is the discrete representation of the output distribution function. It is plotted into a histogram with appropriate sub-interval intervals to obtain the frequency distribution. If normalized to have unit area, this frequency distribution provides an approximation of the probability density distribution function of the output EER.

[0066] Step S4: Statistical analysis and uncertainty assessment

[0067] Perform statistical analysis on the EER sample data generated by the Monte Carlo method to assess the uncertainty of the energy efficiency test results. The specific steps are as follows:

[0068] Statistical mean and standard deviation: For a large number of generated EER value samples, calculate their mean μ and standard deviation σ as the best estimate of EER and measurement uncertainty. The calculation formula is as follows:

[0069]

[0070] Confidence interval calculation: According to the sample distribution of EER values, determine the interval at a certain confidence level. Usually, a 95% or 99% confidence interval is used to ensure the reliability of the system energy efficiency test results. This example uses a 95% confidence interval.

[0071] Distribution morphology analysis: The distribution of EER values ​​is analyzed through the probability density function (PDF) to confirm whether the EER distribution conforms to the normal distribution or other specific distribution, so as to further optimize the evaluation model.

[0072] The calculations in steps 3 and 4 were performed using MATLAB software. The best estimate of EER was 2.3588, the measurement uncertainty was 0.35442, and the probability density calculation results were as follows: Figure 3 shown.

[0073] Embodiment 2:

[0074] The embodiment of the present invention also provides an uncertainty assessment device for automobile thermal management energy efficiency testing, comprising:

[0075] A building module for building an energy efficiency measurement model for a power battery liquid cooling thermal management system;

[0076] A processing module is used to perform probability distribution analysis and outlier determination on key parameters of the energy efficiency test of the power battery liquid cooling thermal management system;

[0077] A simulation module, which is used to perform Monte Carlo simulation based on the key parameters of the power battery liquid cooling thermal management system energy efficiency test and the power battery liquid cooling thermal management system energy efficiency measurement model;

[0078] The evaluation module is used to perform statistical analysis on the EER sample data generated by the Monte Carlo method and evaluate the uncertainty of the energy efficiency test results.

[0079] As an implementation method of an embodiment of the present invention, the energy efficiency measurement model of the power battery liquid cooling thermal management system is:

[0080]

[0081] Among them, Q cooling is the cooling capacity of the system, that is, the amount of heat removed by the thermal management system per unit time; q m is the mass flow rate of the coolant; c is the specific heat capacity of the coolant; T in T is the inlet temperature of the coolant battery pack; out is the outlet temperature of the coolant battery pack; P sys is the power consumption of the system, including the power consumption of the condenser fan, pump, and compressor; P f is the power of the condenser fan; P P is the power of the water pump; P c is the power of the compressor.

[0082] As an implementation method of an embodiment of the present invention, key input parameters include: coolant mass flow rate, coolant specific heat capacity, coolant inlet and outlet temperature difference, and system power consumption.

[0083] Embodiment 3:

[0084] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and when the computer program is run, a method for evaluating uncertainty in an automobile thermal management energy efficiency test is executed.

[0085] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for uncertainty assessment of automotive thermal management energy efficiency testing, characterized in that: include: Step S1, constructing an energy efficiency measurement model for a power battery liquid cooling thermal management system; Step S2, performing probability distribution analysis and outlier determination on key parameters of the energy efficiency test of the power battery liquid cooling thermal management system; Step S3, performing Monte Carlo simulation according to key parameters of the power battery liquid cooling thermal management system energy efficiency test and the power battery liquid cooling thermal management system energy efficiency measurement model; Step S4: Statistically analyze the EER sample data generated by the Monte Carlo method to evaluate the uncertainty of the energy efficiency test results.

2. The uncertainty assessment method for automobile thermal management energy efficiency test according to claim 1, characterized in that: In step S1, the energy efficiency measurement model of the power battery liquid cooling thermal management system is: Among them, Q cooling is the cooling capacity of the system, that is, the amount of heat removed by the thermal management system per unit time; q m is the mass flow rate of the coolant; c is the specific heat capacity of the coolant; T in T is the inlet temperature of the coolant battery pack; out is the outlet temperature of the coolant battery pack; P sys is the power consumption of the system, including the power consumption of the condenser fan, pump, and compressor; P f is the power of the condenser fan; P P is the power of the water pump; P c is the power of the compressor.

3. The uncertainty assessment method for automobile thermal management energy efficiency test according to claim 2, characterized in that: In step S2, key input parameters include: coolant mass flow rate, coolant specific heat capacity, coolant inlet and outlet temperature difference, and system power consumption.

4. An uncertainty assessment device for automobile thermal management energy efficiency testing, characterized in that: include: A building module for building an energy efficiency measurement model for a power battery liquid cooling thermal management system; A processing module is used to perform probability distribution analysis and outlier determination on key parameters of the energy efficiency test of the power battery liquid cooling thermal management system; A simulation module, which is used to perform Monte Carlo simulation based on the key parameters of the power battery liquid cooling thermal management system energy efficiency test and the power battery liquid cooling thermal management system energy efficiency measurement model; The evaluation module is used to perform statistical analysis on the EER sample data generated by the Monte Carlo method and evaluate the uncertainty of the energy efficiency test results.

5. The uncertainty assessment device for automobile thermal management energy efficiency test according to claim 4, characterized in that: The energy efficiency measurement model of the power battery liquid cooling thermal management system is: Among them, Q cooling is the cooling capacity of the system, that is, the amount of heat removed by the thermal management system per unit time; q m is the mass flow rate of the coolant; c is the specific heat capacity of the coolant; T in T is the inlet temperature of the coolant battery pack; out is the outlet temperature of the coolant battery pack; P sys is the power consumption of the system, including the power consumption of the condenser fan, pump, and compressor; P f is the power of the condenser fan; P P is the power of the water pump; P c is the power of the compressor.

6. The uncertainty assessment device for automobile thermal management energy efficiency test according to claim 5, characterized in that: The key input parameters include: coolant mass flow rate, coolant specific heat capacity, coolant inlet and outlet temperature difference, and system power consumption.

7. A storage medium, characterized in that: The storage medium stores a computer program, which, when running, executes the uncertainty assessment method for automotive thermal management energy efficiency testing as described in any one of claims 1 to 3.

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