Power battery thermal management fault diagnosis method, device, equipment and storage medium

Abstract

The present invention belongs to the technical field of fault diagnosis, and discloses a method, device, equipment and storage medium for diagnosing faults in the thermal management of power batteries. When the power battery is in a non-heating state, the present invention obtains the average temperature rise rate of each battery cell in the power battery, judges the average temperature rise rate, and when the average temperature rise rate is greater than or equal to a preset temperature rise rate, evaluates the actual heat exchange amount of the battery pack cooling system based on a thermal management proxy model to obtain an evaluation result. When the actual heat exchange amount is greater than or equal to a preset safe heat exchange amount, the actual heat exchange amount is evaluated according to the maximum cooling capacity of the battery pack cooling system. When the actual heat exchange amount is greater than the maximum cooling capacity, a diagnostic result of the first risk level is output. By using the temperature rise rate of the battery cells, the heat dissipation amount of the battery cooling system, and the maximum cooling capacity of the battery cooling system, the abnormal temperature of the battery pack is diagnosed and classified, which has predictability, is beneficial to improving the safety of battery use, and ensures the life and property safety of passengers.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and particularly to a method, device, equipment and storage medium for diagnosing faults in thermal management of power batteries. Background Art

[0002] A power battery is an important energy storage device for new energy vehicles. Heat is generated during the normal charging and discharging processes of the power battery. When the cooling capacity of the power battery cooling system is lower than the heat generation amount, the battery temperature will rise. Currently, safety strategies are usually designed based on the battery temperature rise rate or the battery temperature limit value, and it is impossible to identify the over-temperature risk in advance according to the actual vehicle conditions and the maximum cooling capacity of the battery cooling system, nor can it identify the abnormal heat generation risk of the power battery in a timely manner according to the actual heat dissipation amount of the battery cooling system.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method, device, equipment and storage medium for diagnosing faults in thermal management of power batteries, aiming to solve the technical problem that thermal runaway occurs in the prior art and has no predictability.

[0005] To achieve the above purpose, the present invention provides a method for diagnosing faults in thermal management of power batteries, and the method includes the following steps:

[0006] When the power battery is in a non-heating state, obtain the average temperature rise rate of each battery cell in the power battery;

[0007] Judge the average temperature rise rate. When the average temperature rise rate is greater than or equal to a preset temperature rise rate, evaluate the actual heat exchange amount of the battery pack cooling system based on a thermal management proxy model to obtain an evaluation result;

[0008] When the evaluation result is that the actual heat exchange amount is greater than or equal to a preset safe heat exchange amount, evaluate the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system. When the actual heat exchange amount is greater than the maximum cooling capacity, output a diagnosis result of the first risk level.

[0009] Optionally, after evaluating the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system when the evaluation result is that the actual heat exchange amount is greater than or equal to the preset safe heat exchange amount, it further includes:

[0010] When the actual heat exchange amount is less than or equal to the maximum cooling capacity, output a diagnosis result of the second risk level.

[0011] Optionally, after determining the average heating rate, the method further includes:

[0012] When the average heating rate is less than the preset heating rate, comparing the actual heat exchange amount with the maximum cooling capacity to obtain a comparison result;

[0013] When the comparison result is that the actual heat exchange amount is greater than the maximum cooling capacity, outputting a diagnostic result of the third risk level.

[0014] Optionally, before evaluating the actual heat exchange amount of the battery pack cooling system based on the thermal management agent model, the method further includes:

[0015] Obtaining a vehicle-end control signal, inputting the vehicle-end control signal into the thermal management simulation model to obtain the cell temperature and the water temperature of the battery cooling circuit, where the vehicle-end control signal at least includes: battery pack SOC, current, voltage, SOH, ambient temperature, cell temperature, pump duty ratio, water valve opening, heat exchanger performance, and air conditioner performance;

[0016] Using the cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model as training data to input into the initial thermal management agent model for training to obtain the thermal management agent model.

[0017] Optionally, evaluating the actual heat exchange amount of the battery pack cooling system based on the thermal management agent model to obtain an evaluation result, including:

[0018] Obtaining a vehicle-end control signal, inputting the vehicle-end control signal into the thermal management agent model to obtain the cell temperature and the water temperature of the battery cooling circuit.

[0019] Optionally, before using the data generated by the thermal management simulation model as training data to input into the initial thermal management agent model for training, the method further includes:

[0020] Determining the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss during the operation of the power battery;

[0021] Determining the total heat generation of the power battery according to the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss;

[0022] Obtaining a power battery heat generation model according to the relationship between the total heat generation of the power battery and the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss.

[0023] Optionally, before inputting the data generated by the thermal management simulation model as training data into the initial thermal management agent model for training, the following steps are further included:

[0024] Obtain the shape factor, convective heat transfer coefficient, convective heat transfer area, coolant temperature, water-cooled plate channel wall temperature, internal thermal conductivity of parts, internal heat conduction area of parts, solid temperature of heat conduction, contact wall temperature between parts, and contact thermal resistance of the power battery cooling system;

[0025] Obtain the convective heat transfer amount based on the shape factor, the convective heat transfer coefficient, the convective heat transfer area, the coolant temperature, and the water-cooled plate channel wall temperature, and obtain a convective heat transfer amount model;

[0026] Obtain the internal conduction heat of parts based on the internal thermal conductivity of parts, the internal heat conduction area of parts, and the solid temperature of heat conduction, and obtain an internal conduction heat model of parts;

[0027] Obtain the conduction heat between parts based on the solid temperature of heat conduction, the contact wall temperature between parts, and the contact thermal resistance, and obtain a conduction heat model between parts;

[0028] Obtain the thermal management simulation model based on the convective heat transfer amount model, the internal conduction heat model of parts, and the conduction heat model between parts.

[0029] In addition, to achieve the above object, the present invention further provides a power battery thermal management fault diagnosis device, and the power battery thermal management fault diagnosis device includes:

[0030] A monitoring module, configured to obtain the average temperature rise rate of each battery cell in the power battery when the power battery is in a non-heating state;

[0031] An evaluation module, configured to judge the average temperature rise rate, and when the average temperature rise rate is greater than or equal to a preset temperature rise rate, evaluate the actual heat transfer amount of the battery pack cooling system based on the thermal management agent model to obtain an evaluation result;

[0032] A diagnosis module, configured to, when the evaluation result is that the actual heat transfer amount is greater than or equal to a preset safe heat transfer amount, evaluate the actual heat transfer amount according to the maximum cooling capacity of the battery pack cooling system, and when the actual heat transfer amount is greater than the maximum cooling capacity, output a diagnosis result of the first risk level.

[0033] In addition, to achieve the above object, the present invention further provides a power battery thermal management fault diagnosis device, and the power battery thermal management fault diagnosis device includes: a memory, a processor, and a power battery thermal management fault diagnosis program stored on the memory and executable on the processor, where the power battery thermal management fault diagnosis program is configured to implement the steps of the power battery thermal management fault diagnosis method as described above.

[0034] In addition, to achieve the above object, the present invention also provides a storage medium, on which a power battery thermal management fault diagnosis program is stored. When the power battery thermal management fault diagnosis program is executed by a processor, the steps of the power battery thermal management fault diagnosis method described above are implemented.

[0035] When the power battery is in a non-heating state, the present invention obtains the average temperature rise rate of each battery cell in the power battery; judges the average temperature rise rate, and when the average temperature rise rate is greater than or equal to a preset temperature rise rate, evaluates the actual heat exchange amount of the battery pack cooling system based on a thermal management agent model to obtain an evaluation result; when the evaluation result is that the actual heat exchange amount is greater than or equal to a preset safe heat exchange amount, evaluates the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system. When the actual heat exchange amount is greater than the maximum cooling capacity, a diagnosis result of the first risk level is output. By diagnosing and grading the abnormal temperature of the battery pack through the battery cell temperature rise rate, the heat dissipation of the battery cooling system, and the maximum cooling capacity of the battery cooling system, it has predictability, which is beneficial to improving the safety of battery use and ensuring the life and property safety of passengers. Description of the Drawings

[0036] Figure 1 is a schematic structural diagram of a power battery thermal management fault diagnosis device in a hardware operating environment related to the embodiment solution of the present invention;

[0037] Figure 2 is a schematic flowchart of the first embodiment of the power battery thermal management fault diagnosis method of the present invention;

[0038] Figure 3 is a schematic diagnosis flowchart of an embodiment of the power battery thermal management fault diagnosis method of the present invention;

[0039] Figure 4 is a schematic flowchart of the second embodiment of the power battery thermal management fault diagnosis method of the present invention;

[0040] Figure 5 is a schematic diagram of agent model data processing in an embodiment of the power battery thermal management fault diagnosis method of the present invention;

[0041] Figure 6 is a structural block diagram of the first embodiment of the power battery thermal management fault diagnosis device of the present invention.

[0042] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0043] It should be understood that the specific embodiments described herein are for explaining the present invention and not for limiting the present invention.

[0044] Refer to Figure 1 , Figure 1 which is a schematic structural diagram of a power battery thermal management fault diagnosis device for the hardware operating environment involved in the solution of the embodiment of the present invention.

[0045] As Figure 1 shown, the power battery thermal management fault diagnosis device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0046] Those skilled in the art can understand that Figure 1 the structure shown in

[0047] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a power battery thermal management fault diagnosis program.

[0048] In Figure 1In the shown power battery thermal management fault diagnosis device, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the power battery thermal management fault diagnosis device of the present invention can be arranged in the power battery thermal management fault diagnosis device. The power battery thermal management fault diagnosis device calls the power battery thermal management fault diagnosis program stored in the memory 1005 through the processor 1001 and executes the power battery thermal management fault diagnosis method provided by the embodiments of the present invention.

[0049] Embodiments of the present invention provide a power battery thermal management fault diagnosis method. Refer to Figure 2 , Figure 2 which is a schematic flowchart of the first embodiment of a power battery thermal management fault diagnosis method of the present invention.

[0050] In this embodiment, the power battery thermal management fault diagnosis method includes the following steps:

[0051] Step S10: When the power battery is in a non-heating state, obtain the average temperature rise rate of each battery cell in the power battery.

[0052] It should be noted that the execution subject of this embodiment is a power battery thermal management fault diagnosis device. Among them, the power battery thermal management fault diagnosis device has functions such as data processing, data communication, and program operation. The power battery thermal management fault diagnosis device can be an integrated controller, a control computer, etc., and of course can also be other devices with similar functions. This embodiment does not make any restrictions on this.

[0053] It can be understood that when the battery pack is in a heating state, the temperature rise rate of the battery pack is relatively high. At this time, due to the interference of charging factors, it is impossible to judge whether the battery pack is normal according to the temperature rise rate of the battery pack. Therefore, when diagnosing the faults of the power battery, it should be carried out when the battery pack is in a non-heating state.

[0054] In specific implementation, when the battery pack is in a non-heating state, it is evaluated based on the average temperature rise rate dT / dt of each battery cell within 1 minute of the battery pack to exclude the influence of large fluctuations in the temperature rise rate of the battery cells under some transient conditions of intense driving on the evaluation. Generally, dT / dt = 0.6 °C / min is the temperature rise rate level when the battery pack is in the heating state, and this value can be set according to the heat exchange performance of the actual battery pack. If dT / dt < 0.6 °C / min, the temperature rise rate of the battery cells is normal; if dT / dt ≥ 0.6 °C / min, the temperature rise rate of the battery cells at this time has exceeded the temperature rise rate when the battery pack is in the heating state, and the temperature rise rate is relatively high. The specific high temperature rise rate is set according to the actual situation, and this embodiment does not limit it. When the battery pack is in the diagnostic state, if the battery pack enters the charging state at this time, the battery pack diagnosis is stopped.

[0055] Step S20: Judge the average temperature rise rate. When the average temperature rise rate is greater than or equal to the preset temperature rise rate, evaluate the actual heat exchange amount of the battery pack cooling system based on the thermal management agent model to obtain an evaluation result.

[0056] In specific implementation, judge the average temperature rise rate. When dT / dt < 0.6 °C / min, the temperature rise rate of the battery pack at this time is relatively low and in a normal state, and no diagnosis is required. If dT / dt ≥ 0.6 °C / min, compare and evaluate the actual heat exchange amount of the battery pack cooling system with the heat exchange amount result of the agent model. dh1′ is the actual heat exchange amount of the battery pack cooling system, which is calculated according to the flow rate Q of the battery pack cooling system L , the inlet temperature T of the battery pack coolant in , the inlet temperature T of the battery pack coolant out , and the duty cycle signal η of the battery water pump. dh1′ = cρQ L (T out - T in ), where c and ρ are the specific heat capacity and density of the coolant, and T in and T out are measured by the temperature sensors in the battery pack cooling pipeline and sent out by the bus signal. Q L is obtained by interpolating the flow MAP values under different coolant water temperatures T in and the duty cycle η of the battery water pump input during the development process. dh1 is the heat exchange amount of the battery pack cooling system calculated by the battery thermal management agent model. And evaluate the actual heat exchange amount of the battery pack cooling system based on the thermal management agent model to obtain an evaluation result.

[0057] Step S30: When the evaluation result is that the actual heat exchange amount is greater than or equal to the preset safe heat exchange amount, evaluate the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system. When the actual heat exchange amount is greater than the maximum cooling capacity, output a diagnosis result of the first risk level.

[0058] In a specific implementation, referring to Figure 3 , Figure 3 is a schematic diagram of the diagnosis process. When evaluating the actual heat exchange amount of the battery pack cooling system based on the thermal management agent model, if at this time, the heat exchange amount of the battery pack cooling system has a large deviation from the normal state. Further, according to the maximum cooling capacity φ maxhex of the battery pack cooling system, evaluate the actual heat exchange amount dh1'. When dh1' > φ maxhex , it means that the maximum cooling capacity of the battery pack cooling system cannot meet the current demand, reminding the user that the battery pack storage state is abnormal and there is a risk of thermal runaway, and the risk level is the first risk level, specifically referring to the existence of a risk of battery thermal runaway.

[0059] Further, when the evaluation result is that the actual heat exchange amount is greater than or equal to the preset safe heat exchange amount, after evaluating the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system, it further includes:

[0060] When the actual heat exchange amount is less than or equal to the maximum cooling capacity, output a diagnosis result of the second risk level.

[0061] In a specific implementation, when dh1' ≤ φ maxhex , it means that the maximum cooling capacity of the battery pack cooling system can meet the current demand, reminding the user that the battery state is abnormal, and output a diagnosis result of the second risk level. The second risk level refers to reminding that the battery state is abnormal.

[0062] Further, after judging the average heating rate, it further includes:

[0063] When the average heating rate is less than the preset heating rate, compare the actual heat exchange amount with the maximum cooling capacity to obtain a comparison result;

[0064] When the comparison result is that the actual heat exchange amount is greater than the maximum cooling capacity, output a diagnosis result of the third risk level.

[0065] In a specific implementation, considering the calculation error of the agent model and the test error of the temperature sensor, when When the actual heat generation of the battery pack is still within the normal range, although the temperature rise rate of the battery cells is high in this case, the heat generation is normal. Usually, when the vehicle is in an intense driving state or a relatively harsh high-temperature environment, it is necessary to evaluate whether the maximum cooling capacity of the battery pack cooling system meets the requirements to avoid the risk of battery overheating caused by intense driving. Among them, φ maxhex is the maximum cooling capacity of the battery cooling system, which is affected by the performance of the air-conditioning system, battery heat exchanger, battery water-cooled plate, water pump, etc. The simulation model can be built through the battery thermal management simulation model, and then the battery pack thermal management agent model calculates it with the ambient temperature, compressor power, vehicle speed, battery water pump duty ratio, etc. in the bus signal as inputs. This will not be elaborated here. If dh1′ ≤ φ maxhex , the maximum cooling capacity of the battery pack cooling system can meet the current requirements, and the diagnostic strategy terminates. If dh1′ > φ maxhex , the maximum cooling capacity of the battery pack cooling system cannot meet the current requirements, and the user needs to be reminded that there is a risk of battery pack overheating in the current environment or working condition, which is the third risk level. Specifically, it is to remind that there is a risk of battery overheating in the current environment or working condition.

[0066] In this embodiment, when the power battery is in a non-heating state, the average temperature rise rate of each battery cell in the power battery is obtained; the average temperature rise rate is judged. When the average temperature rise rate is greater than or equal to the preset temperature rise rate, the actual heat exchange amount of the battery pack cooling system is evaluated based on the thermal management agent model to obtain an evaluation result; when the evaluation result is that the actual heat exchange amount is greater than or equal to the preset safe heat exchange amount, the actual heat exchange amount is evaluated according to the maximum cooling capacity of the battery pack cooling system. When the actual heat exchange amount is greater than the maximum cooling capacity, the diagnostic result of the first risk level is output. The battery pack temperature abnormality is diagnosed and classified through the battery cell temperature rise rate, the heat dissipation amount of the battery cooling system, and the maximum cooling capacity of the battery cooling system, which has predictability and is beneficial to improving the battery usage safety and ensuring the life and property safety of the occupants.

[0067] Reference Figure 4 , Figure 4 is a schematic flowchart of the second embodiment of a method for diagnosing faults in power battery thermal management according to the present invention.

[0068] Based on the above first embodiment, before the step S20 of the method for diagnosing faults in power battery thermal management in this embodiment, it further includes:

[0069] Step S01: Obtain the vehicle-end control signal, input the vehicle-end control signal into the thermal management simulation model, and obtain the battery cell temperature and the water temperature of the battery cooling circuit. The vehicle-end control signal at least includes: battery pack SOC, current, voltage, SOH, ambient temperature, battery cell temperature, water pump duty ratio, water valve opening, heat exchanger performance, and air-conditioning performance;

[0070] Step S02: Input the cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model as training data into the initial thermal management agent model for training to obtain the thermal management agent model.

[0071] In a specific implementation, obtain the vehicle-end control signal, where the vehicle-end control signal at least includes: battery pack SOC, current, voltage, SOH, ambient temperature, cell temperature, water pump duty ratio, water valve opening, heat exchanger performance, and air conditioner performance. Use the vehicle-end control signal as the input data of the thermal management simulation model, so that the thermal management simulation model obtains the cell temperature and the water temperature of the battery cooling circuit according to the vehicle-end control signal, and input the cell temperature and the water temperature of the battery cooling circuit as training data into the initial thermal management agent model for training to obtain the thermal management agent model. When performing diagnosis, input the vehicle-end control signal into the thermal management agent model, specifically referring to Figure 5 , Figure 5 which is a schematic diagram of the data processing of the agent model. Use the signals related to the battery pack thermal management as the input data of the model, and perform calculations through the neurons in several hidden layers of the model to output the output data such as the cell temperature and the water temperature of the battery cooling circuit.

[0072] Furthermore, in order to better describe the battery pack thermal management simulation model, before inputting the data generated by the thermal management simulation model as training data into the initial thermal management agent model for training, it further includes:

[0073] Determine the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss during the operation of the power battery;

[0074] Determine the total heat generation of the power battery according to the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss;

[0075] Obtain a power battery heat generation model according to the relationship between the total heat generation of the power battery and the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss.

[0076] In a specific implementation, for the heat generation of the power battery, a power battery heat generation model can be established. During the charging and discharging operations of the power battery, complex electrochemical reactions will occur, which are related to many factors such as temperature, SOC, charge-discharge rate, and SOH. According to the Faraday loss φ ηfarad , entropy heat loss hysteresis loss φ hyst , ohmic internal resistance loss φ ohm , charge transfer loss φct , the diffusion loss φ diff and the thermal runaway loss φ TR to determine the total heat generation φ of the power battery total , according to the Faraday loss φ ηfarad , the entropy heat loss the hysteresis loss φ hyst , the ohmic internal resistance loss φ ohm , the charge transfer loss φ ct , the diffusion loss φ diff and the thermal runaway loss φ TR to determine the total heat generation φ of the power battery total to obtain the heat generation model of the power battery from the relationship:

[0077]

[0078] Furthermore, before inputting the data generated by the thermal management simulation model into the initial thermal management agent model for training, it further includes:

[0079] Obtain the shape factor, convective heat transfer coefficient, convective heat transfer area, coolant temperature, water-cooled plate channel wall temperature, internal thermal conductivity of the part, internal heat conduction area of the part, thermal conduction solid temperature, contact wall temperature between parts, and contact thermal resistance of the power battery cooling system;

[0080] According to the shape factor, the convective heat transfer coefficient, the convective heat transfer area, the coolant temperature, and the water-cooled plate channel wall temperature, obtain the convective heat transfer amount and obtain the convective heat transfer amount model;

[0081] According to the internal thermal conductivity of the part, the internal heat conduction area of the part, and the thermal conduction solid temperature, obtain the internal heat conduction of the part and obtain the internal heat conduction model of the part;

[0082] According to the thermal conduction solid temperature, the contact wall temperature between parts, and the contact thermal resistance, obtain the heat conduction between parts and obtain the heat conduction model between parts;

[0083] According to the convective heat transfer amount model, the internal heat conduction model of the part, and the heat conduction model between parts, obtain the thermal management simulation model.

[0084] In specific implementations, there are various cooling forms for power batteries. Generally, the most widely used cooling form currently is the water-cooling form. The heat of the power battery is carried away through heat convection with the coolant. At the same time, a convective heat transfer model, the conduction heat model within the part, and the conduction heat model between parts are respectively constructed according to the cooling form. In the convective heat transfer model, dh1 is the convective heat transfer amount, kHeat is the shape factor related to the heat exchange structure, hconv is the convective heat transfer coefficient, A1 is the convective heat transfer area, tFluid is the coolant temperature, and tWall1 is the temperature of the water-cooled plate flow channel wall surface. The convective heat transfer model can be expressed as: dh1 = KHeat·hconv·A1·(tFluid - tWall1).

[0085] For the heat conduction within the water-cooled plate, heat conduction plate, battery module, and battery cell, and the heat conduction between adjacent components, dh2 is the conduction heat within the component, tccond is the thermal conductivity, A2 is the heat conduction area, tWall2 and tWall3 are the solid temperatures of the heat conduction respectively, dh3 is the conduction heat between different components, tWall4 and tWall5 are the contact wall surface temperatures of the two components for heat conduction respectively, and thermRes is the contact thermal resistance. Therefore, the conduction heat model within the part and the conduction heat model between parts are obtained:

[0086] Conduction heat model within the part: dh2 = tccond·A2·(tWall2 - tWall3);

[0087] Conduction heat model between parts: dh3 = (tWall4 - tWall5) / thermRes.

[0088] Finally, the thermal management simulation model is obtained according to the convective heat transfer model, the conduction heat model within the part, and the conduction heat model between parts.

[0089] In this embodiment, by collecting common signals in the vehicle as the input signals of the model, without the need to additionally add sensors for collection, the calculation of the surrogate model and the judgment of the diagnostic strategy can be carried out in the vehicle's vehicle controller or the controller related to thermal management. Therefore, on the basis of not increasing additional costs, the diagnosis and grading of the abnormal temperature of the battery pack are realized through the cell temperature rise rate, the heat dissipation of the battery cooling system, and the maximum cooling capacity of the battery cooling system, and the thermal management of the power battery is predicted, which is beneficial to the safe use of the battery.

[0090] In addition, the embodiment of the present invention also proposes a storage medium, on which a power battery thermal management fault diagnosis program is stored. When the power battery thermal management fault diagnosis program is executed by a processor, the steps of the power battery thermal management fault diagnosis method described above are realized.

[0091] Refer to Figure 6, Figure 6 This is the structural block diagram of the first embodiment of the power battery thermal management fault diagnosis device of the present invention.

[0092] As Figure 6 shown, the power battery thermal management fault diagnosis device proposed in the embodiment of the present invention includes:

[0093] A monitoring module 10, configured to obtain the average temperature rise rate of each battery cell in the power battery when the power battery is in a non-heating state.

[0094] An evaluation module 20, configured to judge the average temperature rise rate, and when the average temperature rise rate is greater than or equal to a preset temperature rise rate, evaluate the actual heat exchange amount of the battery pack cooling system based on a thermal management proxy model to obtain an evaluation result.

[0095] A diagnosis module 30, configured to, when the evaluation result is that the actual heat exchange amount is greater than or equal to a preset safe heat exchange amount, evaluate the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system, and when the actual heat exchange amount is greater than the maximum cooling capacity, output a diagnosis result of the first risk level.

[0096] In this embodiment, when the power battery is in a non-heating state, the average temperature rise rate of each battery cell in the power battery is obtained; the average temperature rise rate is judged, and when the average temperature rise rate is greater than or equal to a preset temperature rise rate, the actual heat exchange amount of the battery pack cooling system is evaluated based on a thermal management proxy model to obtain an evaluation result; when the evaluation result is that the actual heat exchange amount is greater than or equal to a preset safe heat exchange amount, the actual heat exchange amount is evaluated according to the maximum cooling capacity of the battery pack cooling system, and when the actual heat exchange amount is greater than the maximum cooling capacity, a diagnosis result of the first risk level is output. By using the battery cell temperature rise rate, the heat dissipation amount of the battery cooling system, and the maximum cooling capacity of the battery cooling system to diagnose and classify the abnormal temperature of the battery pack, it has predictability, which is beneficial to improving the safety of battery use and ensuring the life and property safety of passengers.

[0097] In one embodiment, the diagnosis module 30 is further configured to output a diagnosis result of the second risk level when the actual heat exchange amount is less than or equal to the maximum cooling capacity.

[0098] In one embodiment, the evaluation module 20 is further configured to compare the actual heat exchange amount with the maximum cooling capacity when the average temperature rise rate is less than the preset temperature rise rate to obtain a comparison result; and when the comparison result is that the actual heat exchange amount is greater than the maximum cooling capacity, output a diagnosis result of the third risk level.

[0099] In one embodiment, the evaluation module 20 is further configured to obtain vehicle-end control signals, input the vehicle-end control signals into the thermal management simulation model to obtain the cell temperature and the water temperature of the battery cooling circuit, where the vehicle-end control signals at least include: battery pack SOC, current, voltage, SOH, ambient temperature, cell temperature, pump duty ratio, water valve opening, heat exchanger performance, and air conditioner performance; and use the cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model as training data to input into the initial thermal management agent model for training to obtain the thermal management agent model.

[0100] In one embodiment, the evaluation module 20 is further configured to obtain vehicle-end control signals, and input the vehicle-end control signals into the thermal management agent model to obtain the cell temperature and the water temperature of the battery cooling circuit.

[0101] In one embodiment, the evaluation module 20 is further configured to determine the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss during the operation of the power battery; determine the total heat generation of the power battery according to the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss; and obtain the power battery heat generation model according to the relationship between the total heat generation of the power battery and the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss.

[0102] In one embodiment, the evaluation module 20 is further configured to obtain the shape factor, convective heat transfer coefficient, convective heat transfer area, coolant temperature, water-cooled plate channel wall temperature, internal thermal conductivity of parts, internal heat conduction area of parts, heat conduction solid temperature, contact wall surface temperature between parts, and contact thermal resistance of the power battery cooling system; obtain the convective heat transfer amount and the convective heat transfer amount model according to the shape factor, the convective heat transfer coefficient, the convective heat transfer area, the coolant temperature, and the water-cooled plate channel wall temperature; obtain the internal heat conduction of parts and the internal heat conduction model of parts according to the internal thermal conductivity of parts, the internal heat conduction area of parts, and the heat conduction solid temperature; obtain the conduction heat between parts and the conduction heat model between parts according to the heat conduction solid temperature, the contact wall surface temperature between parts, and the contact thermal resistance; and obtain the thermal management simulation model according to the convective heat transfer amount model, the internal heat conduction model of parts, and the conduction heat model between parts.

[0103] It should be understood that the above is only an example for illustration and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not make any restrictions in this regard.

[0104] It should be understood that although the steps in the flowcharts in the embodiments of the present application are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order restriction and can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time but can be executed at different times, and their execution order is not necessarily sequential but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0105] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is made here.

[0106] In addition, it should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0107] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0108] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0109] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for diagnosing thermal management faults of a power battery, characterized in that, The method for diagnosing faults in the thermal management of a power battery includes: When the power battery is in a non-heating state, obtain the average temperature rise rate of each battery cell in the power battery; Judge the average temperature rise rate. When the average temperature rise rate is greater than or equal to a preset temperature rise rate, evaluate the actual heat exchange amount of the battery pack cooling system based on a thermal management proxy model to obtain an evaluation result; When the evaluation result is that the actual heat exchange amount is greater than or equal to a preset safe heat exchange amount, evaluate the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system. When the actual heat exchange amount is greater than the maximum cooling capacity, output a diagnostic result of the first risk level; Before evaluating the actual heat exchange amount of the battery pack cooling system based on the thermal management proxy model, it further includes: Obtain a vehicle-end control signal, input the vehicle-end control signal into a thermal management simulation model to obtain the battery cell temperature and the water temperature of the battery cooling circuit. The vehicle-end control signal at least includes: battery pack SOC, current, voltage, SOH, ambient temperature, battery cell temperature, pump duty ratio, water valve opening, heat exchanger performance, and air conditioner performance; Use the battery cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model as training data to input into an initial thermal management proxy model for training to obtain the thermal management proxy model; Before using the battery cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model as training data to input into an initial thermal management proxy model for training, it further includes: Determine the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss when the power battery is working; Determine the total heat generation of the power battery according to the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss; Obtain a power battery heat generation model according to the relationship between the total heat generation of the power battery and the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss.

2. The method according to claim 1, characterized in that, After evaluating the actual heat exchange amount according to the maximum cooling capacity of the battery pack cooling system when the evaluation result is that the actual heat exchange amount is greater than or equal to a preset safe heat exchange amount, it further includes: When the actual heat exchange amount is less than or equal to the maximum cooling capacity, output a diagnostic result of the second risk level.

3. The method according to claim 1, wherein After judging the average temperature rise rate, it further includes: When the average temperature rise rate is less than the preset temperature rise rate, compare the actual heat exchange amount with the maximum cooling capacity to obtain a comparison result; When the comparison result is that the actual heat exchange amount is greater than the maximum cooling capacity, output a diagnostic result of the third risk level.

4. The method according to claim 1, wherein Evaluating the actual heat exchange amount of the battery pack cooling system based on the thermal management proxy model to obtain an evaluation result includes: Obtain a vehicle-end control signal, input the vehicle-end control signal into the thermal management proxy model to obtain the battery cell temperature and the water temperature of the battery cooling circuit.

5. The method according to claim 1, wherein The step of inputting the cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model into the initial thermal management agent model for training further includes: Obtaining the shape factor, convective heat transfer coefficient, convective heat transfer area, coolant temperature, water-cooled plate channel wall temperature, internal thermal conductivity of parts, internal heat conduction area of parts, heat conduction solid temperature, contact wall temperature between parts, and contact thermal resistance of the power battery cooling system; Obtaining the convective heat transfer amount according to the shape factor, the convective heat transfer coefficient, the convective heat transfer area, the coolant temperature, and the water-cooled plate channel wall temperature, and obtaining a convective heat transfer amount model; Obtaining the internal conduction heat of the parts according to the internal thermal conductivity of the parts, the internal heat conduction area of the parts, and the heat conduction solid temperature, and obtaining an internal conduction heat model of the parts; Obtaining the conduction heat between parts according to the heat conduction solid temperature, the contact wall temperature between parts, and the contact thermal resistance, and obtaining a conduction heat model between parts; Obtaining the thermal management simulation model according to the convective heat transfer amount model, the internal conduction heat model of the parts, and the conduction heat model between parts; 6. A power battery thermal management fault diagnosis device, characterized in that, The power battery thermal management fault diagnosis device includes: A monitoring module, configured to obtain the average temperature rise rate of each cell in the power battery when the power battery is in a non-heating state; An evaluation module, configured to judge the average temperature rise rate, and when the average temperature rise rate is greater than or equal to a preset temperature rise rate, evaluate the actual heat transfer amount of the battery pack cooling system based on the thermal management agent model to obtain an evaluation result; A diagnosis module, configured to, when the evaluation result is that the actual heat transfer amount is greater than or equal to a preset safe heat transfer amount, evaluate the actual heat transfer amount according to the maximum cooling capacity of the battery pack cooling system, and when the actual heat transfer amount is greater than the maximum cooling capacity, output a diagnosis result of the first risk level; Before evaluating the actual heat transfer amount of the battery pack cooling system based on the thermal management agent model, it further includes: Obtaining a vehicle-end control signal, inputting the vehicle-end control signal into the thermal management simulation model to obtain the cell temperature and the water temperature of the battery cooling circuit, where the vehicle-end control signal at least includes: battery pack SOC, current, voltage, SOH, ambient temperature, cell temperature, water pump duty ratio, water valve opening, heat exchanger performance, and air conditioner performance; Inputting the cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model into the initial thermal management agent model for training to obtain the thermal management agent model; Before inputting the cell temperature and the water temperature of the battery cooling circuit generated by the thermal management simulation model into the initial thermal management agent model for training, it further includes: Determining the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss when the power battery is working; Determining the total battery heat generation of the power battery according to the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss; A heat generation model of the power battery is obtained according to the relationship between the total heat generation of the power battery and the Faraday loss, entropy heat loss, hysteresis loss, ohmic internal resistance loss, charge transfer loss, diffusion loss, and thermal runaway loss.

7. A power battery thermal management fault diagnosis device, characterized in that The device includes: a memory, a processor, and a power battery thermal management fault diagnosis program stored on the memory and executable on the processor, where the power battery thermal management fault diagnosis program is configured to implement the steps of the power battery thermal management fault diagnosis method according to any one of claims 1 to 5.

8. A storage medium, characterized in that, A power battery thermal management fault diagnosis program is stored on the storage medium, and when the power battery thermal management fault diagnosis program is executed by a processor, the steps of the power battery thermal management fault diagnosis method according to any one of claims 1 to 5 are implemented.

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