Detection method for integrated thermal management module of new energy vehicle

Abstract

The present invention belongs to the field of integrated thermal management, involves data analysis technology, is used for solving the problem that existing detection methods for integrated thermal management modules of new energy vehicles cannot troubleshoot causes of an anomaly when a battery temperature is abnormal, and particularly relates to a detection method for integrated thermal management modules of new energy vehicles, comprising the following steps: measuring and analyzing a battery temperature of a new energy vehicle: generating a measurement cycle, segmenting the measurement cycle into a plurality of measurement time periods, and acquiring a measurement coefficient JC of a battery pack of the new energy vehicle during the measurement time periods; and comparing the measurement coefficient JC of the battery pack of the new energy vehicle during the measurement time periods with a measurement threshold JCmax, and by means of a comparison result, determining whether the temperature of the battery pack of the new energy vehicle during the measurement periods meets requirements. The present invention can measure and analyze the battery temperature of new energy vehicles, can feed back the extent of temperature anomalies in battery packs by means of measurement coefficients, and issues early warnings in time when the battery pack temperature is abnormal.

Description

A detection method for thermal management integrated module of new energy vehicles Technical Field

[0001] The present invention belongs to the field of thermal management integration and relates to data analysis technology, and specifically is a detection method for a thermal management integrated module of a new energy vehicle. Background Art

[0002] Thermal management integrated modules can help systems manage complex thermal issues. By integrating cooling systems, they effectively control internal system temperatures and ensure that every electronic component operates at an optimal temperature. Furthermore, by optimizing the system's cooling solution, they improve overall system efficiency and reduce energy consumption.

[0003] The existing detection method for thermal management integrated modules of new energy vehicles is unable to troubleshoot the cause of abnormal battery temperature, resulting in a cumbersome and inefficient handling process for temperature abnormalities.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to provide a new energy vehicle thermal management integrated module detection method to solve the problem that the existing new energy vehicle thermal management integrated module detection method is unable to troubleshoot the abnormal cause when the battery temperature is abnormal;

[0006] The technical problem to be solved by the present invention is: how to provide a new energy vehicle thermal management integrated module detection method that can troubleshoot the abnormal cause when the battery temperature is abnormal.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for detecting a thermal management integrated module of a new energy vehicle comprises the following steps:

[0009] Step 1: Detect and analyze the battery temperature of the new energy vehicle: Generate a detection cycle, divide the detection cycle into several detection time periods, and obtain the detection coefficient JC of the new energy vehicle battery pack during the detection period;

[0010] Step 2: Obtain the detection threshold JCmax through the storage module, compare the detection coefficient JC of the new energy vehicle battery pack during the detection period with the detection threshold JCmax, and determine whether the temperature of the new energy vehicle battery pack during the detection period meets the requirements based on the comparison result;

[0011] Step 3: Analyze the cause of abnormal battery pack temperature of new energy vehicles: obtain the average speed value of the battery exhaust fan of the new energy vehicle during the detection period and mark it as the wind speed value, obtain the wind speed threshold through the storage module, compare the wind speed value with the wind speed threshold, and determine the cause of abnormal battery pack temperature based on the comparison result.

[0012] As a preferred embodiment of the present invention, the process of obtaining the detection coefficient JC of the new energy vehicle battery pack during the detection period includes: marking the battery cells of the new energy vehicle battery pack as detection objects i, i = 1, 2, ..., n, n is a positive integer, obtaining the maximum surface temperature and the minimum surface temperature of the detection object i during the detection period and marking them as high temperature value WGi and low temperature value WDi respectively, marking the maximum value of the high temperature value WGi of all detection objects i as high temperature performance value GB, and marking the minimum value of the low temperature value WDi of all detection objects i as low temperature performance value DB; through the formula The detection coefficient JC of the new energy vehicle battery pack during the detection period is obtained.

[0013] As a preferred embodiment of the present invention, the specific process of comparing the detection coefficient JC of the new energy vehicle battery pack during the detection period with the detection threshold JCmax includes: if the detection coefficient JC is less than the detection threshold JCmax, it is determined that the temperature of the new energy vehicle thermal battery pack during the detection period meets the requirements; if the detection coefficient JC is greater than or equal to the detection threshold JCmax, it is determined that the temperature of the new energy vehicle battery pack during the detection period does not meet the requirements, an abnormal analysis signal is generated and the abnormal analysis signal is sent to the detection platform, and after receiving the abnormal analysis signal, the detection platform sends the abnormal analysis signal to the abnormal analysis module.

[0014] As a preferred embodiment of the present invention, the specific process of comparing the wind speed value with the wind speed threshold includes: if the wind speed value is less than the wind speed threshold, it is determined that the cause of the abnormal battery pack temperature is a fan heat dissipation failure, a heat dissipation fault signal is generated and the heat dissipation fault signal is sent to the detection platform, and after receiving the heat dissipation fault signal, the detection platform sends the heat dissipation fault signal to the mobile phone terminal of the manager; if the wind speed value is greater than or equal to the wind speed threshold, the maximum value of the voltage value of the detection object i within the detection period is marked as the voltage height value YGi, and the voltage height values ​​YGi of all detection objects i constitute a voltage height set, and the variance of the voltage height set is calculated to obtain the group pressure deviation value, and the group pressure deviation threshold is obtained through the storage module, and the group pressure deviation value is compared with the group pressure deviation threshold and a voltage balancing signal or a thermal management maintenance signal is generated through the comparison result.

[0015] As a preferred embodiment of the present invention, the specific process of comparing the group pressure deviation value with the group pressure deviation threshold includes: if the group pressure deviation value is greater than or equal to the group pressure deviation threshold, it is determined that the cause of the abnormal battery pack temperature is voltage deviation, a voltage balancing signal is generated and the voltage balancing signal is sent to the detection platform, and after the detection platform receives the voltage balancing signal, the voltage balancing signal is sent to the mobile phone terminal of the administrator; if the group pressure deviation value is less than the group pressure deviation threshold, it is determined that the cause of the abnormal battery pack temperature is thermal management failure, a thermal management maintenance signal is generated and the thermal management maintenance signal is sent to the detection platform, and after the detection platform receives the thermal management maintenance signal, the thermal management maintenance signal is sent to the mobile phone terminal of the administrator.

[0016] As a preferred embodiment of the present invention, the present invention is applied to a new energy vehicle thermal management integrated module detection system, which includes a detection platform, which is communicatively connected to a battery detection module, an abnormality analysis module, and a storage module;

[0017] The battery detection module is used to detect and analyze the battery temperature of the new energy vehicle and send an abnormal analysis signal to the abnormal analysis module through the detection platform when the temperature does not meet the requirements;

[0018] The abnormality analysis module is used to analyze the cause of abnormal temperature of the battery pack of the new energy vehicle and generate a heat exhaust fault signal, a voltage balancing signal or a thermal management maintenance signal to be sent to the detection platform.

[0019] The present invention has the following beneficial effects:

[0020] 1. The battery detection module can detect and analyze the battery temperature of new energy vehicles. By detecting the temperature of each battery cell in the new energy vehicle battery pack one by one, and then comprehensively analyzing and calculating the temperature detection data of all battery cells in different time periods to obtain the detection coefficient, the detection coefficient is used to provide feedback on the abnormal degree of battery pack temperature and to issue a timely warning when the battery pack temperature is abnormal;

[0021] 2. The abnormality analysis module can analyze the causes of abnormal battery pack temperature in new energy vehicles. By detecting the speed of the battery heat exhaust fan of the new energy vehicle during the detection period, the numerical value of the wind speed is used to provide feedback on the degree of correlation between the heat exhaust system and the abnormal battery pack temperature. The factors that cause abnormal battery pack temperature are marked to improve the efficiency of temperature anomaly processing.

[0022] 3. The voltage deviation value is obtained by comprehensively analyzing the voltage of all battery cells. The correlation between voltage balance and abnormal battery pack temperature is fed back through the voltage deviation value, thereby marking the cause of abnormal battery pack temperature as voltage imbalance or thermal management integrated module failure, and realizing the fault factor troubleshooting when the battery pack temperature is abnormal. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] FIG1 is a system block diagram of a first embodiment of the present invention;

[0025] FIG2 is a flow chart of a method according to a second embodiment of the present invention. DETAILED DESCRIPTION

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] Example 1

[0028] As shown in FIG1 , a new energy vehicle thermal management integrated module detection system includes a detection platform, which is communicatively connected to a battery detection module, an abnormality analysis module, and a storage module.

[0029] The battery detection module is used to detect and analyze the battery temperature of new energy vehicles: the battery cells of the new energy vehicle battery pack are marked as detection objects i, i = 1, 2, ..., n, n is a positive integer, a detection cycle is generated, the detection cycle is divided into several detection time periods, the maximum surface temperature and the minimum surface temperature of the detection object i in the detection time period are obtained and marked as high temperature value WGi and low temperature value WDi respectively, the maximum value of the high temperature value WGi of all detection objects i is marked as high temperature performance value GB, and the minimum value of the low temperature value WDi of all detection objects i is marked as low temperature performance value DB; through the formula A detection coefficient JC of the new energy vehicle battery pack during the detection period is obtained, wherein α1 and α2 are both proportional coefficients, and α1>α2>1; a detection threshold JCmax is obtained through the storage module, and the detection coefficient JC of the new energy vehicle battery pack during the detection period is compared with the detection threshold JCmax: if the detection coefficient JC is less than the detection threshold JCmax, it is determined that the temperature of the new energy vehicle thermal battery pack during the detection period meets the requirements; if the detection coefficient JC is greater than or equal to the detection threshold JCmax, it is determined that the temperature of the new energy vehicle battery pack during the detection period does not meet the requirements, an abnormal analysis signal is generated and sent to the detection platform, and the detection platform sends the abnormal analysis signal to the abnormal analysis module after receiving the abnormal analysis signal; the battery temperature of the new energy vehicle is detected and analyzed, and the temperature of the battery cells of the new energy vehicle battery pack is detected one by one, and then the temperature detection data of all battery cells are comprehensively analyzed and calculated in different time periods to obtain the detection coefficient, and the abnormal degree of the battery pack temperature is fed back through the detection coefficient, and an early warning is issued in time when the battery pack temperature is abnormal.

[0030] The abnormality analysis module is used to analyze the cause of abnormal temperature of the battery pack of the new energy vehicle: obtain the average speed value of the battery exhaust fan of the new energy vehicle during the detection period and mark it as the wind speed value, obtain the wind speed threshold through the storage module, and compare the wind speed value with the wind speed threshold: if the wind speed value is less than the wind speed threshold, it is determined that the cause of the abnormal temperature of the battery pack is a fan exhaust failure, generate a heat exhaust failure signal and send the heat exhaust failure signal to the detection platform. After receiving the heat exhaust failure signal, the detection platform sends the heat exhaust failure signal to the mobile phone terminal of the manager, and analyzes the cause of the abnormal temperature of the battery pack of the new energy vehicle. By detecting the speed value of the battery exhaust fan of the new energy vehicle during the detection period, the correlation between the heat exhaust system and the abnormal temperature of the battery pack is fed back through the value of the wind speed value, and the factors that cause the abnormal temperature of the battery pack are marked to improve the efficiency of processing the abnormal temperature; if the wind speed value is greater than or equal to the wind speed threshold, the maximum value of the voltage value of the detection object i during the detection period is marked as the high voltage value YGi, and the high voltage value Y of all detection objects i is used. Gi constitutes a voltage height set, and the variance of the voltage height set is calculated to obtain a group pressure deviation value. The group pressure deviation threshold is obtained through the storage module, and the group pressure deviation value is compared with the group pressure deviation threshold: if the group pressure deviation value is greater than or equal to the group pressure deviation threshold, it is determined that the cause of the abnormal battery pack temperature is voltage deviation, and a voltage balancing signal is generated and sent to the detection platform. After receiving the voltage balancing signal, the detection platform sends the voltage balancing signal to the administrator's mobile terminal; if the group pressure deviation value is less than the group pressure deviation threshold, it is determined that the cause of the abnormal battery pack temperature is a thermal management fault, and a thermal management maintenance signal is generated and sent to the detection platform. After receiving the thermal management maintenance signal, the detection platform sends the thermal management maintenance signal to the administrator's mobile terminal; a comprehensive analysis of the voltages of all battery cells is performed to obtain a group pressure deviation value, and the group pressure deviation value is used to provide feedback on the degree of correlation between voltage balancing and abnormal battery pack temperature, thereby marking the cause of the abnormal battery pack temperature as voltage imbalance or thermal management integrated module failure, thereby realizing fault factor troubleshooting when the battery pack temperature is abnormal.

[0031] Example 2

[0032] As shown in FIG2 , a method for detecting a thermal management integrated module of a new energy vehicle includes the following steps:

[0033] Step 1: Detect and analyze the battery temperature of the new energy vehicle: Generate a detection cycle, divide the detection cycle into several detection time periods, and obtain the detection coefficient JC of the new energy vehicle battery pack during the detection period;

[0034] Step 2: Obtain the detection threshold JCmax through the storage module, compare the detection coefficient JC of the new energy vehicle battery pack during the detection period with the detection threshold JCmax, and determine whether the temperature of the new energy vehicle battery pack during the detection period meets the requirements based on the comparison result;

[0035] Step 3: Analyze the cause of abnormal battery pack temperature of new energy vehicles: obtain the average speed value of the battery exhaust fan of the new energy vehicle during the detection period and mark it as the wind speed value, obtain the wind speed threshold through the storage module, compare the wind speed value with the wind speed threshold, and determine the cause of abnormal battery pack temperature based on the comparison result.

[0036] A new energy vehicle thermal management integrated module detection method, when in operation, generates a detection cycle, divides the detection cycle into several detection time periods, obtains the detection coefficient JC of the new energy vehicle battery pack within the detection period; obtains the detection threshold JCmax through a storage module, compares the detection coefficient JC of the new energy vehicle battery pack within the detection period with the detection threshold JCmax, and determines whether the temperature of the new energy vehicle battery pack within the detection period meets the requirements based on the comparison result; obtains the average value of the speed value of the battery heat exhaust fan of the new energy vehicle within the detection period and marks it as the wind speed value, obtains the wind speed threshold through the storage module, compares the wind speed value with the wind speed threshold, and determines the cause of the abnormal battery pack temperature based on the comparison result.

[0037] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0038] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by those skilled in the art according to the actual situation; for example: A technician in this field collected multiple sets of sample data and set corresponding detection coefficients for each set of sample data. The set detection coefficients and the collected sample data were substituted into the formula. Any three formulas formed a three-variable linear equation system. The calculated coefficients were screened and averaged, resulting in values ​​of α1 and α2 of 2.35 and 1.47, respectively.

[0039] The size of the coefficient is to quantify each parameter to obtain a specific numerical value, which is convenient for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding detection coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the detection coefficient is proportional to the value of the temperature high performance value.

[0040] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0041] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for detecting a thermal management integrated module of a new energy vehicle, characterized in that: The following steps are involved: Step 1: Detect and analyze the battery temperature of the new energy vehicle: generate a detection cycle, divide the detection cycle into several detection time periods, and obtain the detection coefficient JC of the new energy vehicle battery pack within the detection period; Step 2: Obtain the detection threshold JCmax through the storage module, compare the detection coefficient JC of the new energy vehicle battery pack during the detection period with the detection threshold JCmax, and determine whether the temperature of the new energy vehicle battery pack during the detection period meets the requirements based on the comparison result; Step 3: Analyze the cause of abnormal battery pack temperature of new energy vehicles: obtain the average speed value of the battery heat exhaust fan of the new energy vehicle during the detection period and mark it as the wind speed value, obtain the wind speed threshold through the storage module, compare the wind speed value with the wind speed threshold, and determine the cause of abnormal battery pack temperature based on the comparison result.

2. A new energy vehicle thermal management integrated module detection method according to claim 1, characterized in that: The process of obtaining the detection coefficient JC of the new energy vehicle battery pack during the detection period includes: marking the battery cells of the new energy vehicle battery pack as detection object i, i=1, 2, ..., n, n is a positive integer, obtaining the maximum value and the minimum value of the surface temperature of the detection object i during the detection period and marking them as high temperature value WGi and low temperature value WDi respectively, marking the maximum value of the high temperature value WGi of all detection objects i as high temperature performance value GB, and marking the minimum value of the low temperature value WDi of all detection objects i as low temperature performance value DB; through the formula The detection coefficient JC of the new energy vehicle battery pack during the detection period is obtained.

3. A new energy vehicle thermal management integrated module detection method according to claim 2, characterized in that: The specific process of comparing the detection coefficient JC of the new energy vehicle battery pack during the detection period with the detection threshold JCmax includes: if the detection coefficient JC is less than the detection threshold JCmax, it is determined that the temperature of the new energy vehicle thermal battery pack during the detection period meets the requirements; if the detection coefficient JC is greater than or equal to the detection threshold JCmax, it is determined that the temperature of the new energy vehicle battery pack during the detection period does not meet the requirements, an abnormal analysis signal is generated and sent to the detection platform, and the detection platform sends the abnormal analysis signal to the abnormal analysis module after receiving the abnormal analysis signal.

4. A new energy vehicle thermal management integrated module detection method according to claim 3, characterized in that: The specific process of comparing the wind speed value with the wind speed threshold includes: if the wind speed value is less than the wind speed threshold, it is determined that the abnormal temperature of the battery pack is caused by a fan heat dissipation failure, a heat dissipation fault signal is generated and the heat dissipation fault signal is sent to the detection platform, and the detection platform sends the heat dissipation fault signal to the mobile phone terminal of the manager after receiving the heat dissipation fault signal; if the wind speed value is greater than or equal to the wind speed threshold, the maximum value of the voltage value of the detection object i within the detection period is marked as the voltage height value YGi, and the voltage height values ​​YGi of all detection objects i constitute a voltage height set, and the variance of the voltage height set is calculated to obtain the group pressure deviation value, and the group pressure deviation threshold is obtained through the storage module, and the group pressure deviation value is compared with the group pressure deviation threshold, and a voltage balancing signal or a thermal management maintenance signal is generated through the comparison result.

5. A new energy vehicle thermal management integrated module detection method according to claim 4, characterized in that: The specific process of comparing the group pressure deviation value with the group pressure deviation threshold includes: if the group pressure deviation value is greater than or equal to the group pressure deviation threshold, it is determined that the abnormal temperature of the battery pack is caused by voltage deviation, a voltage balancing signal is generated and the voltage balancing signal is sent to the detection platform, and the detection platform sends the voltage balancing signal to the mobile phone terminal of the manager after receiving the voltage balancing signal; if the group pressure deviation value is less than the group pressure deviation threshold, it is determined that the abnormal temperature of the battery pack is caused by thermal management failure, a thermal management maintenance signal is generated and the thermal management maintenance signal is sent to the detection platform, and the detection platform sends the thermal management maintenance signal to the mobile phone terminal of the manager after receiving the thermal management maintenance signal.

6. A new energy vehicle thermal management integrated module detection method according to any one of claims 1 to 5, characterized in that: Applied to the new energy vehicle thermal management integrated module detection system, the new energy vehicle thermal management integrated module detection system includes a detection platform, the detection platform is communicatively connected with a battery detection module, an abnormality analysis module and a storage module; The battery detection module is used to detect and analyze the battery temperature of the new energy vehicle and send an abnormal analysis signal to the abnormal analysis module through the detection platform when the temperature does not meet the requirements; The abnormality analysis module is used to analyze the cause of abnormal temperature of the battery pack of the new energy vehicle and generate a heat exhaust fault signal, a voltage balancing signal or a thermal management maintenance signal to send to the detection platform.

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